GECCO 2021

Main Page Proceedings Companion Compilation Author Index



Companion Volume Table of Contents

Companion Volume Front Matter | Companion Volume Back Matter

Chair's Welcome
Krzysztof Krawiec (GECCO 2021 General Chair, Poznan University of Technology, Poznań, Poland)


Competition Entries Workshops
Competition Evolutionary Computation in the Energy Domain: Smart Grid Applications Workshop Analysing Algorithmic Behaviour of Optimisation Heuristics
Competition on the Optimal Camera Placement Problem (OCP) and the Unicost Set Covering Problem (USCP) Workshop Black Box Optimization Benchmarking
Competition Open Optimization Competition 2021: Competition and Benchmarking of Sampling-Based Optimization Algorithms Workshop Decomposition Techniques in Evolutionary Optimization
Competition Bound Constrained Single Objective Numerical Optimization Workshop Evolutionary Algorithms and HPC
Competition Optimization of a simulation model for a capacity and resource planning task for hospitals under special consideration of the COVID-19 pandemic Workshop Evolutionary Algorithms for Problems with Uncertainty
Hot Off the Press Workshop Evolutionary Computation and Decision Making
Late-Breaking Abstracts Workshop Evolutionary Computation for the Automated Design of Algorithms
Posters Workshop Evolutionary Computation for Permutation Problems
Ant Colony Optimization and Swarm Intelligence Workshop Evolutionary Data Mining and Optimization over Graphs
Complex Systems (Artificial Life, Artificial Immune Systems, Generative and Developmental Systems, Evolutionary Robotics, Evolvable Hardware) Workshop Evolutionary Reinforcement Learning
Evolutionary Combinatorial Optimization and Metaheuristics Workshop Evolutionary Computation Software Systems
Evolutionary Machine Learning Workshop International Workshop on Learning Classifier Systems
Evolutionary Numerical Optimization Workshop Landscape-Aware Heuristic Search
Genetic Algorithms Workshop Neuroevolution at Work
General Evolutionary Computation and Hybrids Workshop Parallel and Distributed Evolutionary Inspired Methods
Genetic Programming Workshop Real-World Applications of Continuous and Mixed-Integer Optimization
Neuroevolution Workshop Surrogate-Assisted Evolutionary Optimisation
Real World Applications Workshop Genetic and Evolutionary Computation in Defense, Security, and Risk Management
Search-Based Software Engineering Workshop Swarm Intelligence Algorithms: Foundations, Perspectives and Challenges
Theory Workshop Visualisation Methods in Genetic and Evolutionary Computation
Tutorials Student Workshop
Introductory Tutorials
Advanced Tutorials
Specialized Tutorials
Specialized Tutorials



Competition Entry (back to top)


Competition Evolutionary Computation in the Energy Domain: Smart Grid Applications (back to top)

Ring Cellular Encode-Decode UMDA: Simple is effective (Page 1)
Ansel Y. Rodríguez-González (CICESE-UT3, CONACYT); Samantha Barajas (Universidad Autónoma de Nayarit); Ramón Aranda (CICESE-UT3, CONACYT); Yoan Martínez-López (Universidad del Camagüey, Universidad Central de Las Villas); and Julio Madera-Quintana (Universidad del Camagüey)

Cooperative Co-evolution Strategies with Time-dependent Grouping for Optimization Problems in Smart Grids (Page 3)
Junpeng Su and Han Huang (South China University of Technology) and Zhifeng Hao (Foshan University)


Competition on the Optimal Camera Placement Problem (OCP) and the Unicost Set Covering Problem (USCP) (back to top)

Exact and Approximate USCP With Branch and Bound (Page 5)
Janez Radešček and Matjaž Depolli (Jožef Stefan Institute)


Competition Open Optimization Competition 2021: Competition and Benchmarking of Sampling-Based Optimization Algorithms (back to top)

Benchmarking Gradient-Free Optimizers for 3D Performance Capture in the Nevergrad platform (Page 7)
Alexandros Doumanoglou, Nikolaos Zioulis, Vladimiros Sterzentsenko, Antonis Karakottas, Dimitrios Zarpalas, and Petros Daras (Centre For Research and Technology HELLAS)

Robust Benchmarking for Multi-Objective Optimization (Page 9)
Tome Eftimov and Peter Korošec (Jožef Stefan Institute)


Competition Bound Constrained Single Objective Numerical Optimization (back to top)

SOMA-CLP for Competition on Bound Constrained Single Objective Numerical Optimization Benchmark (Page 11)
Tomas Kadavy, Michal Pluhacek, Adam Viktorin, and Roman Senkerik (Tomas Bata University in Zlin)


Competition Optimization of a simulation model for a capacity and resource planning task for hospitals under special consideration of the COVID-19 pandemic (back to top)

Hospital Simulation Model Optimisation with a Random ReLU Expansion Surrogate Model (Page 13)
Laurens Bliek (Technical University of Eindhoven); Arthur Guijt (Centrum Wiskunde & Informatica (CWI), Delft University of Technology); and Rickard Karlsson (Chalmers University of Technology)

Surrogate-based Optimisation for a Hospital Simulation Scenario Using Pairwise Classifiers (Page 15)
Pablo Naharro (Universidad Politécnica de Madrid, Lurtis Ltd); Jose María Peña (Lurtis Ltd, jm.penya@lurtis.com); and Antonio LaTorre (Universidad Politécnica de Madrid)

An Evolutionary and Neighborhood-based Algorithm for Optimization under Low Budget Requirements (Page 17)
Jordi Pereira (Universidad Adolfo Ibáñez)

Linear Regression Strategy for Differential Evolution (Page 19)
José Luis Sainz-Pardo Auñón (University Miguel Hernández, Center of Operations Research)


Hot Off the Press (back to top)

Do Quality Indicators Prefer Particular Multi-Objective Search Algorithms in Search-Based Software Engineering? (Hot Off the Press track at GECCO 2021) (Page 21)
Shaukat Ali (Simula Research Laboratory); Paolo Arcaini (National Institute of Informatics); and Tao Yue (Nanjing University of Aeronautics and Astronautics, China and Simula Research Laboratory, Norway)

Theoretical Analyses of Multi-Objective Evolutionary Algorithms on Multi-Modal Objectives (Hot-off-the-Press Track at GECCO 2021) (Page 25)
Benjamin Doerr (École Polytechnique, CNRS) and Weijie Zheng (Southern University of Science and Technology, University of Science and Technology of China)

Runtime Analysis via Symmetry Arguments (Hot-off-the-Press Track at GECCO 2021) (Page 23)
Benjamin Doerr (Ecole Polytechnique, Laboratoire d'Informatique (LIX))

Reducing Bias in Multi-Objective Optimization Benchmarking (Page 27)
Tome Eftimov and Peter Korošec (Jožef Stefan Institute)

Optimal Recombination and Adaptive Restarts Improve GA Performance on the Asymmetric TSP (Page 29)
Anton Eremeev and Yulia Kovalenko (Institute of Scientific Information for Social Sciences RAS, Dostoevsky Omsk State University)

Genetic Improvement of Data for Maths Functions (Page 31)
w. b. langdon (University College London) and Oliver Krauss (University of Applied Sciences Upper Austria)

Achieving Weight Coverage for an Autonomous Driving System with Search-based Test Generation (HOP Track at GECCO 2021) (Page 33)
Thomas Laurent (Lero & University College Dublin), Paolo Arcaini and Fuyuki Ishikawa (National Institute of Informatics), and Anthony Ventresque (Lero & University College Dublin)

Genetic Improvement of Routing in Delay Tolerant Networks (Page 35)
Michela Lorandi, Leonardo Lucio Custode, and Giovanni Iacca (University of Trento)

Interactive Parameter Tuning of Bi-objective Optimisation Algorithms Using the Empirical Attainment Function (Page 37)
Manuel López-Ibáñez (Universidad de Málaga) and Juan Esteban Diaz (Universidad San Francisco de Quito)

The Influence of Uncertainties on Optimization of Vaccinations on a Network of Animal Movements (Page 39)
Krzysztof Michalak (Wroclaw University of Economics) and Mario Giacobini (University of Torino)

Multi-Objective Parameter-less Population Pyramid in Solving the Real-World and Theoretical Problems (Page 41)
Michal Witold Przewozniczek (Wroclaw University of Science and Technology), Piotr Dziurzanski (West Pomeranian University of Technology), and Shuai Zhao and Leandro Soares Indrusiak (The University of York)

On Sampling Error in Evolutionary Algorithms (Page 43)
Dirk Schweim, David Wittenberg, and Franz Rothlauf (Johannes Gutenberg University)

Improving Assertion Oracles with Evolutionary Computation (Page 45)
Valerio Terragni (University of Auckland), Gunel Jahangirova (Università della Svizzera italiana), Mauro Pezzè (Università della Svizzera italiana & Schaffhausen Institute of Technology), and Paolo Tonella (Università della Svizzera italiana)

Analysis of Evolutionary Algorithms on Fitness Function with Time-linkage Property (Hot-off-the-Press Track at GECCO 2021) (Page 47)
Weijie Zheng (Southern University of Science and Technology, University of Science and Technology of China); Huanhuan Chen (University of Science and Technology of China); and Xin Yao (Southern University of Science and Technology)


Late-Breaking Abstract (back to top)

An Improved Predictor of Daily Stock Index Based on a Genetic Filter (Page 49)
Dong-Hee Cho, Seung-Hyun Moon, and Yong-Hyuk Kim (Kwangwoon Univ.)

Algorithm Selection using Transfer Learning (Page 51)
Niranjana Deshpande and Naveen Sharma (Rochester Institute of Technology)

A software library for archiving nondominated points (Page 53)
Duarte Manuel Dias, Alexandre Daniel Jesus, and Luis Paquete (University of Coimbra)

An Interactive Tool for Enhancing Hospital Capacity Predictions Using an Epidemiological Model (Page 55)
Finley Gibson (Swansea University, University of Exeter) and Rhodri Fabbro, Alma Rahat, Thomas Torsney-Weir, Daniel Archambault, Michael Gravenor, and Biagio Lucini (Swansea University)

A new Hybrid Evolutionary Algorithm for Dial-A-Ride Problems (Page 57)
Sonia Nasri (Business Higher School), Hend Bouziri (Higher School of Economics and Business), and Wassila Aggoune-Matalaa (Luxembourg Institute of Science and Technology)

Generative Design of Microfluidic Channel Geometry Using Evolutionary Approach (Page 59)
Nikolay Nikitin, Alexander Hvatov, Iana Polonskaia, and Anna Kalyuzhnaya (ITMO University) and Georgii Grigorev, Xiaohao Wang, and Xiang Qian (National Tsing Hua University)

Rapid Prototyping of Evolution-Driven Biclustering Methods in Julia (Page 61)
Paweł Renc (AGH University of Science and Technology); Patryk Orzechowski (University of Pennsylvania, AGH University of Science and Technology); Jarosław Wąs and Aleksander Byrski (AGH University of Science and Technology); and Jason H. Moore (University of Pennsylvania)

k-Pareto Optimality for Many-Objective Genetic Optimization (Page 63)
Jean Ruppert (Mathematics and Computing S.à.r.l.) and Marharyta Aleksandrova and Thomas Engel (University of Luxembourg)

Winner Prediction for Real-time Strategy Games through Feature Selection Based on a Genetic Wrapper (Page 65)
Seung-Soo Shin and Yong-Hyuk Kim (Kwangwoon Univ.)


Poster (back to top)


Ant Colony Optimization and Swarm Intelligence (back to top)

Novelty Particle Swarm Optimisation for Truss Optimisation Problems (Page 67)
Hirad Assimi, Frank Neumann, and Markus Wagner (University of Adelaide) and Xiaodong Li (RMIT University)

Partial-ACO as a GA Mutation Operator Applied to TSP Instances (Page 69)
Darren M. Chitty (Aston University)

On detecting the novelties in metaphor-based algorithms (Page 71)
Iztok Jr. Fister and Iztok Fister (University of Maribor) and Andres Iglesias and Akemi Galvez (University of Cantabria)

Evolved Response Thresholds Generalize Across Problem Instances for a Deterministic-Response Multiagent System (Page 73)
H. David Mathias (University of Wisconsin - La Crosse), Annie S. Wu (University of Central Florida), and Daniel Dang (Whitman College)

Ant Colony Optimization for Energy-Efficient Train Operations (Page 75)
Federico Naldini (Alma Mater Studiorum - Università di Bologna) and Paola Pellegrini and Joaquin Rodrigurez (Université Gustave Eiffel)

Learning Assignment Order In An Ant Colony Optimiser For The University Course Timetabling Problem (Page 77)
James Sakal, Jonathan Fieldsend, and Edward Keedwell (University of Exeter)

Ant Swarm Algorithm for Self-Organizing Complex System (Page 79)
Juntao Zhang (Huazhong University of Science and Technology) and Peng Cheng (Coolanyp, LLC)


Complex Systems (Artificial Life, Artificial Immune Systems, Generative and Developmental Systems, Evolutionary Robotics, Evolvable Hardware) (back to top)

Predicting soft robot’s locomotion fitness (Page 81)
Renata Biaggi Biazzi (Bioinformatics Graduate Program, University of Sao Paulo); André Fujita (University of Sao Paulo); and Daniel Yasumasa Takahashi (ICe - UFRN)

On the use of feature-maps for improved quality-diversity meta-evolution (Page 83)
David Mark Bossens and Danesh Tarapore (University of Southampton)
Additional Supplemental Material

Promoting Reproductive Isolation Through Diversity in On-line Collective Robotics (Page 85)
Amine Boumaza (Université de Lorraine / LORIA)

Younger Is Better: A Simple and Efficient Selection Strategy for MAP-Elites (Page 87)
Alex Coninx and Stephane Doncieux (Sorbonne Universite; CNRS, ISIR)

Ad hoc Teaming Through Evolution (Page 89)
Joshua Cook and Kagan Tumer (Oregon State University)

The Impact of Different Tasks on Evolved Robot Morphologies (Page 91)
Matteo De Carlo (Vrije Universiteit Amsterdam); Eliseo Ferrante (Vrije Universiteit Amsterdam, Technology Innovation Institute); and Jacintha Ellers, Gerben Meynen, and A. E. Eiben (Vrije Universiteit Amsterdam)
Additional Supplemental Material

Comparing lifetime learning methods for morphologically evolving robots (Page 93)
Fuda van Diggelen, Guszti Eiben, and Eliseo Ferrante (VU University Amsterdam)

Heterogeneous Agent Coordination via Adaptive Quality Diversity and Specialization (Page 95)
Gaurav Dixit, Charles Koll, and Kagan Tumer (Oregon State University)

Reinforcement Learning with Rare Significant Events: Direct Policy Search vs. Gradient Policy Search (Page 97)
Paul Ecoffet and Nicolas Fontbonne (Sorbonne Université, CNRS); Jean-Baptiste André (ENS Ulm, CNRS); and Nicolas Bredeche (Sorbonne Université, CNRS)

Automatic Exploration of the Property Space of Reservoirs (Page 99)
Mika Ito, Leo Cazenille, and Nathanael Aubert-Kato (Ochanomizu University)

Examining Forms of Inductive Bias Towards `Simplicity' in Genetic Algorithms to Enhance Evolvability of Boolean Functions (Page 101)
Hetvi Jethwani and Sumeet Agarwal (Indian Institute of Technology Delhi)

Designing Fitness Functions for Odour Source Localisation (Page 103)
João Macedo, Lino Marques, and Ernesto Costa (University of Coimbra)

How to Evolve a Neuron (Page 105)
Garrett Mitchener (College of Charleston)
Additional Supplemental Material

Environmental Impact on Evolving Language Diversity (Page 107)
Geoff Nitschke and Gregory Furman (University of Cape Town)

Impact of Energy Efficiency on the Morphology and Behaviour of Evolved Robots (Page 109)
Margarita Alejandra Rebolledo Coy (TH Köln, VU University Amsterdam); Daan Zeeuwe (VU University Amsterdam); Thomas Bartz-Beielstein (TH Köln); and A.E. Eiben (VU University Amsterdam)
Additional Supplemental Material

Illuminating the Space of Beatable Lode Runner Levels Produced By Various Generative Adversarial Networks (Page 111)
Kirby Steckel and Jacob Schrum (Southwestern University)

Growing Simulated Robots with Environmental Feedback: an Eco-Evo-Devo Approach (Page 113)
Kathryn Walker (IT University of Copenhagen), Helmut Hauser (University of Bristol), and Sebastian Risi (IT University of Copenhagen)
Additional Supplemental Material

Pathogen Dose based Natural Killer Cell Algorithm for Classification (Page 115)
Dongmei Wang, Yiwen Liang, Chengyu Tan, Hongbin Dong, and Xinmin Yang (Wuhan University)
Additional Supplemental Material


Evolutionary Combinatorial Optimization and Metaheuristics (back to top)

Optimizing a GPU-Accelerated Genetic Algorithm for the Vehicle Routing Problem (Page 117)
Marwan Fouad Abdelatti, Abdeltawab Hendawi, and Manbir Singh Sodhi (University of Rhode Island)

Linear representation of categorical values (Page 119)
Arnaud Berny (Independent researcher)

Effective Recombination Operators for the family of Vehicle Routing Problems (Page 121)
Piotr Cybula (University of Lodz, Faculty of Mathematics and Computer Science; Emapa S.A.); Andrzej Jaszkiewicz (Poznan University of Technology, Faculty of Computing and Telecommunications,); Przemysław Pełka (Emapa S.A.); and Marek Rogalski and Piotr Sielski (University of Lodz, Faculty of Mathematics and Computer Science; Emapa S.A.)

Introducing a Hash Function for the Travelling Salesman Problem for Differentiating Solutions (Page 123)
Mehdi El Krari (Univ. Lille, Centrale Lille); Rym Nesrine Guibadj (Université du Littoral Côte d'Opale); John Woodward (Queen Mary University of London); and Denis Robilliard (Université du Littoral Côte d'Opale)

Automated Configuration of Parallel Machine Dispatching Rules by Machine Learning (Page 125)
Georg Faustmann (TU Wien), Christoph Mrkvicka (MCP GmbH), and Nysret Musliu and Felix Winter (TU Wien)

Selecting Between Evolutionary and Classical Algorithms for the CVRP Using Machine Learning (Page 127)
Justin C. Fellers, Jose D. Quevedo, and Marwan F. Abdelatti (University of Rhode Island); Meghan K. Steinhaus (The Coast Guard Academy); and Manbir S. Sodhi (University of Rhode Island)

The Optimal Filtering set Problem with Application to Surrogate Evaluation in Genetic Programming (Page 129)
Francisco J. Gil-Gala, María R. Sierra, Carlos Mencía, and Ramiro Varela (University of Oviedo)

Optimisation Algorithms for Parallel Machine Scheduling Problems with Setup Times (Page 131)
Fabian Kittel and Jannik Enenkel (Technische Hochschule Mittelhessen), Jana Holznigenkemper (Philipps-Universität Marburg), Neil Urquhart (Edinburgh Napier University), and Michael Guckert (Technische Hochschule Mittelhessen)

Stochastic Local Search for Efficient Hybrid Feature Selection (Page 133)
Ole Jakob Mengshoel (Norwegian University of Science and Technology, Carnegie Mellon University); Tong Yu (Carnegie Mellon University); Jon Riege (Norwegian University of Science and Technology); and Eirik Flogard (Norwegian University of Science and Technology, Arbeidstilsynet)
Additional Supplemental Material

A Grouping Genetic Algorithm for the Unrelated Parallel-Machine Scheduling Problem (Page 135)
Octavio Ramos-Figueroa and Marcela Quiroz-Castellanos (Universidad Veracruzana)

Error Function Learning with Interpretable Compositional Networks for Constraint-Based Local Search (Page 137)
Florian Richoux (AIST) and Jean-François Baffier (The University of Tokyo)
Additional Supplemental Material

A Hybrid Local Search Framework for the Dynamic Capacitated Arc Routing Problem (Page 139)
Hao Tong and Leandro L. Minku (University of Birmingham), Stefan Menzel and Bernhard Sendhoff (HRI-EU), and Xin Yao (Southern University of Science and Technology)

Continuous Encoding for Community Detection in Complex Networks (Page 141)
Wei Zheng, Yiqing Zhang, and Jianyong Sun (Xi'an Jiaotong University)


Evolutionary Machine Learning (back to top)

Detecting Anomalies in Spacecraft Telemetry Using Evolutionary Thresholding and LSTMs (Page 143)
Pawel Benecki (KP Labs, Silesian University of Technology); Szymon Piechaczek (KP Labs); Daniel Kostrzewa (KP Labs, Silesian University of Technology); and Jakub Nalepa (Silesian University of Technology, KP Labs)

Improved Evolution of Generative Adversarial Networks (Page 145)
Victor Costa, Nuno Lourenço, João Correia, and Penousal Machado (University of Coimbra)

Sparsity-based Evolutionary Multi-objective Feature Selection for Multi-label Classification (Page 147)
Kaan Demir, Bach Hoai Nguyen, Bing Xue, and Mengjie Zhang (Victoria University of Wellington)

Scatter Search for high-dimensional feature selection using feature grouping (Page 149)
Miguel García Torres (Universidad Pablo de Olavide, Universidad Americana); Diego P. Pinto Roa and José Luis Vázquez Noguera (Universidad Americana); Federico Divina and Francisco Gómez Vela (Universidad Pablo de Olavide); and Julio C. Mello Román (Univesidad Americana, Universidad Nacional de Concepción)

Meta-Learning for Symbolic Hyperparameter Defaults (Page 151)
Pieter Gijsbers (Department Mathematics and Computer Science, Technical University of Eindhoven); Florian Pfisterer (LMU Munich, Deptartment of Statistics, Munich Germany); Jan N. van Rijn (LIACS, Leiden University); Bernd Bischl (LMU Munich, Deptartment of Statistics, Munich Germany); and Joaquin Vanschoren (Department Mathematics and Computer Science, Technical University of Eindhoven)
Additional Supplemental Material

Evo-RL: Evolutionary-Driven Reinforcement Learning (Page 153)
Ahmed Hallawa (RWTH Aachen University, University Hospital Aachen); Thorsten Born and Anke Schmeink (RWTH Aachen University); Guido Dartmann (University of Applied Sciences Trier); Arne Peine and Lukas Martin (University Hospital Aachen); Giovanni Iacca (University of Trento); A.E. Eiben (Vrije Universiteit Amsterdam); and Gerd Ascheid (RWTH Aachen University)
Additional Supplemental Material

Understanding evolutionary induction of decision trees: A multi-tree repository approach (Page 155)
Krzysztof Jurczuk, Marcin Czajkowski, and Marek Kretowski (Bialystok University of Technology)

Growth and Harvest Induce Essential Dynamics in Neural Networks (Page 157)
Ilona Kulikovskikh (Samara University) and Tarzan Legović (Institute of Applied Ecology, Oikon Ltd.; Libertas International University)

Permutation-based Optimization using a Generative Adversarial Network (Page 159)
Sami Lemtenneche (Ouargla University) and Abdelhakim Cheriet and Abdellah Bensayah (Ouargla university)

EvolMusic: Towards Musical Adversarial Examples for Black-Box Attacks on Speech-To-Text (Page 161)
Mariele Motta (neurocat GmbH), Tanja Hagemann and Sebastian Fischer (Telekom Innovation Laboratories), and Felix Assion (neurocat GmbH)
Additional Supplemental Material

Explainability and Performance of Anticipatory Learning Classifier Systems in Non-Deterministic Environments (Page 163)
Romain Orhand and Anne Jeannin-Girardon (Icube Laboratory, University of Strasbourg); Pierre Parrend (Icube Laboratory, ECAM Strasbourg-Europe); and Pierre Collet (Icube Laboratory, University of Strasbourg)

Multi-objective Genetic Programming for Symbolic Regression with the Adaptive Weighted Splines Representation (Page 165)
Christian Raymond, Qi Chen, Bing Xue, and Mengjie Zhang (Victoria University of Wellington)

An Evolutionary Approach to Interpretable Learning (Page 167)
Jake Robertson and Ting Hu (Queen's University)

Misclassification Detection based on Conditional VAE for Rule Evolution in Learning Classifier System (Page 169)
Hiroki Shiraishi, Masakazu Tadokoro, Yohei Hayamizu, Yukiko Fukumoto, Hiroyuki Sato, and Keiki Takadama (The University of Electro-Communications)

Adopting Lexicase Selection for Michigan-Style Learning Classifier Systems with Continuous-Valued Inputs (Page 171)
Alexander R. M. Wagner and Anthony Stein (University of Hohenheim)

Evolving Local Interpretable Model-agnostic Explanations for Deep Neural Networks in Image Classification (Page 173)
Bin Wang, Wenbin Pei, Bing Xue, and Mengjie Zhang (Victoria University of Wellington)

Adaptive Multi-Fitness Learning for Robust Coordination (Page 175)
Connor Yates, Ayhan Alp Aydeniz, and Kagan Tumer (Oregon State University)


Evolutionary Multiobjective Optimization (back to top)

MOMPA: a high performance multi-objective optimizer based on marine predator algorithm. (Page 177)
Long Chen, Xuebing Cai, Kezhong Jin, and Zhenzhou Tang (Wenzhou University)

The Effect of Offspring Population Size on NSGA-II: A Preliminary Study (Page 179)
Max Benjamin Hort and Federica Sarro (University College London)

Dynamic Adaptation of Decomposition Vector Set Size for MOEA/D (Page 181)
Yuta Kobayashi, Claus Aranha, and Tetsuya Sakurai (University of Tsukuba)

Multi-Criteria Differential Evolution: Treating Multitask Optimization as Multi-Criteria Optimization (Page 183)
Jian-Yuian Li (South China University of Technology, Pazhou Laboratory); Ke-Jing Du (Victoria University); Zhi-Hui Zhan (South China University of Technology, Pazhou Laboratory); and Hua Wang and Jun Zhang (Victoria University)

An Approximate MIP-DoM Calculation for Multi-objective Optimization using Affinity Propagation Clustering Algorithm (Page 185)
Cláudio Lúcio do Val Lopes (CEFET-MG, A3Data); Flávio V. Cruzeiro Martins (CEFET-MG); Elizabeth Fialho Wanner (CEFET-MG, Aston University); and Kalyanmoy Deb (Michigan State University)

Generating Multi-Objective Bilevel Optimization Problems with Multiple Non-Cooperative Followers (Page 187)
Jesus-Adolfo Mejia-de-Dios and Efren Mezura-Montes (University of Veracruz)

Labeling-Oriented Non-Dominated Sorting is $\Theta(MN^3)$ (Page 189)
Sumit Mishra and Ved Prakash (Indian Institute of Information Technology Guwahati) and Maxim Buzdalov (ITMO University)

A Niching Framework based on Fitness Proportionate Sharing for Multi-Objective Genetic Algorithm (MOGA-FPS) (Page 191)
Abdul-Rauf Nuhu and Xuyang Yan (North Carolina A&T State University), Daniel Opoku (Kwame Nkrumah University of Science and Technology), and Abdollah Homaifar (North Carolina Agricultural and Technical State University)
Additional Supplemental Material

Landmark-Based Multi-Objective Route Planning for Large-scale Road Net (Page 193)
Jiaze Sun and Nan Han (Xi'an University of Posts and Telecommunications), Jianbin Huang (Xidian University), and Jiahui Deng (Northwest University)

Multi-Objective Last Step Preference Bayesian Optimization (Page 195)
Juan Ignacio Ungredda and Juergen Branke (University of Warwick) and Mariapia Marchi and Teresa Montrone (ESTECO SpA)

Two Comprehensive Performance Metrics for Overcoming the Deficiencies of IGD and HV (Page 197)
Liping Wang, Lin Zhang, and Yu Ren (Zhejiang University of Technology); Qicang Qiu (Zhejiang Lab); and Feiyue Qiu (Zhejiang University of Technology)


Evolutionary Numerical Optimization (back to top)

Estimation of von Mises-Fisher Distribution Algorithm, with application to support vector classification (Page 199)
Adetunji David Ajimakin and V. Susheela Devi (Indian Institute of Science)

Reinforcement Learning for Dynamic Optimization Problems (Page 201)
Abdennour Boulesnane (Faculty of Medicine, Salah Boubnider University) and Souham Meshoul (Princess Nourah Bint Abdulrahman University RC-CCIS)

A Empirical Study of Cooperative Frequency in Cooperative Co-evolution (Page 203)
Ling-Yu Li and Wen-Jie Ou (South China University of Technology), Xiao-Min Hu (Guangdong University of Technology), and Wei-Neng Chen and An Song (South China University of Technology)

Disease Outbreaks: Tuning Predictive Machine Learning (Page 205)
Geoff Nitschke and Amina Abdullahi (University of Cape Town)

Setup Of Fuzzy Hybrid Particle Swarms: A Heuristic Approach (Page 207)
Nicolas Georges Roy (University of Namur, Cenaero); Charlotte Beauthier (Cenaero); and Timotéo Carletti and Alexandre Mayer (University of Namur)
Additional Supplemental Material

CMA-ES with Coordinate Selection for High-Dimensional and Ill-Conditioned Functions (Page 209)
Hiroki Shimizu (The University of Tokyo) and Masashi Toyoda (Institute of Industrial Science, the University of Tokyo)
Additional Supplemental Material

Bridging Kriging Believer and Expected Improvement Using Bump Hunting for Expensive Black-box Optimization (Page 211)
Bing Wang, Hemant Kumar Singh, and Tapabrata Ray (University of New South Wales)

Automated Feature Detection of Black-Box Continuous Search-Landscapes using Neural Image Recognition (Page 213)
Boris Yazmir (The Galilee Research Institute - Migal) and Ofer M. Shir (Tel-Hai College, The Galilee Research Institute - Migal)
Additional Supplemental Material

A Complementarity Analysis of the COCO Benchmark Problems and Artificially Generated Problems (Page 215)
Urban Škvorc (Jožef Stefan Institute, Jožef Stefan International Postgraduate School) and Tome Eftimov and Peter Korošec (Jožef Stefan Institute)


Genetic Algorithms (back to top)

Three population co-evolution for generating mechanics of endless runner games (Page 217)
Vojtěch Černý and Jakub Gemrot (Charles University)

Quantum Genetic Selection (Page 219)
Giovanni Acampora (University of Naples Federico II; Istituto Nazionale di Fisica Nucleare, Sezione di Napoli) and Roberto Schiattarella and Autilia Vitiello (University of Naples Federico II)

Fitness Value Curves Prediction in the Evolutionary Process of Genetic Algorithms (Page 221)
Renuá Meireles Almeida, Denys Menfredy Ferreira Ribeiro, Rodrigo Moraes Rodrigues, and Otávio Noura Teixeira (Federal University of Pará)

A Genetic Algorithm Approach to Compute Mixed Strategy Solutions for General Stackelberg Games (Page 223)
Srivathsa Gottipati and Praveen Paruchuri (International Institute of Information Technology)

ALF – A Fitness-Based Artificial Life Form for Evolving Large-Scale Neural Networks (Page 225)
Rune Krauss, Marcel Merten, and Mirco Bockholt (Institute of Computer Science, University of Bremen) and Rolf Drechsler (Group of Computer Architecture, University of Bremen; Cyber-Physical Systems, DFKI GmbH)

Optimization of Multi-Objective Mixed-Integer Problems with a Model-Based Evolutionary Algorithm in a Black-Box Setting (Page 227)
Krzysztof Leszek Sadowski and Dirk Thierens (Utrecht University) and Peter A.N. Bosman (Centrum Wiskunde & Informatica (CWI))

A Benchmark Generator of Tree Decomposition Mk Landscapes (Page 229)
Dirk Thierens and Tobias van Driessel (Utrecht University)

It's the Journey Not the Destination; Building Genetic Algorithms Practitioners Can Trust (Page 231)
Jakub Vincalek, Sean Walton, and Ben Evans (Swansea University)


General Evolutionary Computation and Hybrids (back to top)

A Crossover That Matches Diverse Parents Together in Evolutionary Algorithms (Page 233)
Maciej Świechowski (QED Software; Faculty of Mathematics and Information Sciences, Warsaw University of Technology)

A Multimethod Approach to Multimodal Function Optimization (Page 235)
Fredrik Foss (Norwegian University of Science and Technology) and Ole Jakob Mengshoel (Norwegian University of Science and Technology, Carnegie Mellon University)

Elo-based Similar-Strength Opponent Sampling for Multiobjective Competitive Coevolution (Page 237)
Sean N. Harris and Daniel R. Tauritz (Auburn University)

OPTION: OPTmization Algorithm Benchmarking ONtology (Page 239)
Ana Kostovska (Jozef Stefan Institute, Jozef Stefan International Postgraduate School); Diederick Vermetten (Leiden Institute for Advanced Computer Science); Carola Doerr (CNRS, Sorbonne University); Sašo Džeroski and Panče Panov (Jozef Stefan Institute, Jozef Stefan International Postgraduate School); and Tome Eftimov (Jozef Stefan Institute)

Learning Multiple Defaults for Machine Learning Algorithms (Page 241)
Florian Pfisterer (LMU Munich, Deptartment of Statistics, Munich Germany); Jan N. van Rijn (LIACS, Leiden University); Philipp Probst (N/A); Andreas Mueller (Microsoft); and Bernd Bischl (LMU Munich, Deptartment of Statistics, Munich Germany)

The Factory Must Grow: Automation in Factorio (Page 243)
Kenneth N. Reid, Iliya Miralavy, Stephen Kelly, Wolfgang Banzhaf, and Cedric Gondro (Michigan State University)

Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Configuration (Page 245)
Furong Ye (Leiden University); Carola Doerr (CNRS, Sorbonne Université); and Thomas Bäck (Leiden University)

Empirical Study of Correlations in the Fitness Landscapes of Combinatorial Optimization Problems (Page 247)
Longfei Zhang (University of Electronic Science and Technology of China), Ke Li (University of Exeter), and Shi Gu (University of Electronic Science and Technology of China)


Genetic Programming (back to top)

Genetic Programming with A New Representation and A New Mutation Operator for Image Classification (Page 249)
Qinglan Fan, Ying Bi, Bing Xue, and Mengjie Zhang (Victoria University of Wellington)

Empirical Analysis of Variance for Genetic Programming based Symbolic Regression (Page 251)
Lukas Kammerer (University of Applied Sciences Upper Austria, Johannes Kepler University); Gabriel Kronberger (University of Applied Sciences Upper Austria); and Stephan Winkler (University of Applied Sciences Upper Austria, Johannes Kepler University)

Fitness First and Fatherless Crossover (Page 253)
w. b. langdon (University College London)

"Re-ID BUFF": An Enhanced Similarity Measurement Based on Genetic Programming for Person Re-identification (Page 255)
Yiming Li and Lin Shang (Nanjing University)

Linear-dependent Multi-interpretation Neuro-Encoded Expression Programming (Page 257)
Jun Ma, Fenghui Gao, Shuangrong Liu, and Lin Wang (University of Jinan)

Principled quality diversity for ensemble classifiers using MAP-Elites (Page 259)
Kyle L. Nickerson (Memorial University of Newfoundland) and Ting Hu (Queens University)

Improving Estimation of Distribution Genetic Programming with Novelty Initialization (Page 261)
Christian Olmscheid, David Wittenberg, Dominik Sobania, and Franz Rothlauf (University of Mainz)

GLEAM: Genetic Learning by Extraction and Absorption of Modules (Page 263)
Anil Kumar Saini (University of Massachusetts Amherst) and Lee Spector (Amherst College, Hampshire College)

Improving the Generalisation of Genetic Programming Models with Evaluation Time and Asynchronous Parallel Computing (Page 265)
Aliyu Sani Sambo, R. Muhammad Atif Azad, and Yevgeniya Kovalchuk (Birmingham City University, School of Computing & Digital Technology); Vivek Padmanaabhan Indramohan (Birmingham City University, School of Health Science); and Hanifa Shah (Birmingham City University; Faculty of Computing, Engineering and the Built Environment)
Additional Supplemental Material

Neurally Guided Transfer Learning for Genetic Programming (Page 267)
Alexander Wild and Barry Porter (Lancaster University)
Additional Supplemental Material

Adversarial Bandit Gene Expression Programming for Symbolic Regression (Page 269)
Congwen Xu (China University of Petroleum Beijing), Qiang Lu (China University of Petroleum), Jake Luo (University of Wisconsin Milwaukee), and Zhiguang Wang (China University of Petroleum Beijing)


Neuroevolution (back to top)

Evolving Reservoir Weights in the Frequency Domain (Page 271)
Sebastian Basterrech (VSB-Technical University of Ostrava) and Gerardo Rubino (Inria)

Evolving Transformer Architecture for Neural Machine Translation (Page 273)
Ben Feng, Dayiheng Liu, and Yanan Sun (Sichuan University)

Growth and Evolution of Deep Neural Networks from Gene Regulatory Networks (Page 275)
Colin Flynn, Mohammed Bennamoun, and Farid Boussaid (University of Western Australia)

A NEAT-based Multiclass Classification Method with Class Binarization (Page 277)
Zhenyu Gao (VU University Amsterdam) and Gongjin Lan (Southern University of Science and Technology)

On the Exploitation of Neuroevolutionary Information (Page 279)
Unai Garciarena (University of the Basque Country), Nuno Lourenço and Penousal Machado (University of Coimbra), and Roberto Santana and Alexander Mendiburu (University of the Basque Country)

Modeling the Evolution of Retina Neural Network (Page 281)
Ziyi Gong and Paul Munro (University of Pittsburgh)

A Coevolutionary Approach to Deep Multi-Agent Reinforcement Learning (Page 283)
Daan Klijn and Guszti Eiben (VU University Amsterdam)

Evolving Neuronal Plasticity Rules using Cartesian Genetic Programming (Page 285)
Henrik Daniel Mettler (University of Bern); Maximilian Schmidt (RIKEN Center for Brain Science, Tokyo); Walter Senn (University of Bern); Mihai Alexandru Petrovici (University of Bern, Heidelberg University); and Jakob Jordan (University of Bern)

A Transfer Learning Based Evolutionary Deep Learning Framework to Evolve Convolutional Neural Networks (Page 287)
Bin Wang, Bing Xue, and Mengjie Zhang (Victoria University of Wellington)

Neuroevolution of a Recurrent Neural Network for Spatial and Working Memory in a Simulated Robotic Environment (Page 289)
Xinyun Zou (University of California, Irvine); Eric Scott (George Mason University); Alexander Johnson (University of California, San Diego); Kexin Chen (University of California, Irvine); Douglas Nitz (University of California, San Diego); Kenneth De Jong (George Mason University); and Jeffrey Krichmar (University of California, Irvine)


Real World Applications (back to top)

Selecting Miners within Blockchain-based Systems Using Evolutionary Algorithms for Energy Optimisation (Page 291)
Akram Alofi (University of Birmingham, Umm Al-Qura University); Mahmoud Bokhari (Taibah University, The University of Adelaide); and Robert Hendley and Rami Bahsoon (University of Birmingham)

Resource Planning for Hospitals Under Special Consideration of the COVID-19 Pandemic: Optimization and Sensitivity Analysis (Page 293)
Thomas Bartz-Beielstein, Marcel Dröscher, Alpar Gür, Alexander Hinterleitner, Olaf Mersmann, Dessislava Peeva, Lennard Reese, Nicolas Rehbach, Frederik Rehbach, Amrita Sen, Aleksandr Subbotin, and Martin Zaefferer (TH Köln)

Risk Aware Optimization of Water Sensor Placement (Page 295)
Antonio Candelieri, Andrea Ponti, and Francesco Archetti (University of Milano-Bicocca)

Distributed Evolutionary Design of HIFU Treatment Plans (Page 297)
Jakub Chlebik and Jiri Jaros (Brno University of Technology)

An Optimal Oil Skimmer Assignment Based on a Genetic Algorithm with Minimal Mobilized Locations (Page 299)
Dong-Hee Cho and Yong-Hyuk Kim (Kwangwoon Univ.)

Diagnosing Autonomous Vehicle Driving Criteria with an Adversarial Evolutionary Algorithm (Page 301)
Mark Coletti, Shang Gao, Spencer Paulissen, Quentin Haas, and Robert Patton (Oak Ridge National Laboratory)

Optimizing the Parameters of A Physical Exercise Dose-Response Model: An Algorithmic Comparison (Page 303)
Mark Connor (University College Dublin, University of Suffolk) and Michael O'Neill (University College Dublin)

Dealing With a Problematic Roundabout by Optimizing a Traffic Light System Through Evolutionary Computation (Page 305)
Francisco Cruz-Zelante, Eduardo Segredo, and Gara Miranda (Universidad de La Laguna)

Weighted Ensemble of Gross Error Detection methods based on Particle Swarm Optimization (Page 307)
Daniel Dobos, Thanh Tien Nguyen, and John McCall (School of Computing, Robert Gordon University) and Allan Wilson, Phil Stockton, and Helen Corbett (Accord-ESL)

Wastewater Systems Planned Maintenance Scheduling Using Multi-Objective Optimisation (Page 309)
Sabrina Draude and Edward Keedwell (University of Exeter); Rebecca Hiscock (Welsh Water); and Zoran Kapelan (University of Exeter, Delft University of Technology)

Evolving Potential Field Parameters For Deploying UAV-based Two-hop Wireless Mesh Networks (Page 311)
Rahul Dubey and Sushil J. Louis (University of Nevada Reno)

ARCH-Elites: Quality-Diversity for Urban Design (Page 313)
Theodoros Galanos and Antonios Liapis (University of Malta); Georgios N. Yannakakis (University of Malta, Technical University of Crete); and Reinhard Koenig (Bauhaus-University Weimar, AIT Austrian Institute of Technology)

Optimising the Introduction of Connected and Autonomous Vehicles in a Public Transport System using Macro-Level Mobility Simulations and Evolutionary Algorithms (Page 315)
Kate Han, Lee Ashley Christie, Alexandru-Ciprian Z\u{a}voianu, and John W. McCall (Robert Gordon University)

Structural Damage Identification under Non-linear EOV Effects Using Genetic Programming (Page 317)
Mohsen Mousavi and Amir H. Gandomi (University of Technology Sydney) and Magd Abdel Wahab (Ghent University)

Towards Higher Order Fairness Functionals for Smooth Path Planning (Page 319)
Victor Parque (Waseda University)

Novelty Search for Evolving Interesting Character Mechanics for a Two-Player Video Game (Page 321)
Eirik Høgdahl Skjærseth and Harald Vinje (Norwegian University of Science and Technology) and Ole Jakob Mengshoel (Norwegian University of Science and Technology, Carnegie Mellon University)
Additional Supplemental Material

Optimising Pheromone Communication in a UAV Swarm (Page 323)
Daniel H. Stolfi, Matthias R. Brust, Grégoire Danoy, and Pascal Bouvry (University of Luxembourg)

A New Pathway to Approximate Energy Expenditure and Recovery of an Athlete (Page 325)
Fabian Clemens Weigend (Western Sydney University), Jason Siegler (Arizona State University), and Oliver Obst (Western Sydney University)
Additional Supplemental Material

Unit-aware Multi-objective Genetic Programming for the Prediction of the Stokes Flow around a Sphere (Page 327)
Heiner Zille, Fabien Evrard, Sanaz Mostaghim, and Berend van Wachem (Otto von Guericke University Magdeburg)
Additional Supplemental Material


Search-Based Software Engineering (back to top)

Neurogenetic Programming Framework for Explainable Reinforcement Learning (Page 329)
Vadim Liventsev (Technical University of Eindhoven, Philips Research); Aki Härmä (Philips Research); and Milan Petković (Technical University of Eindhoven, Philips Research)

Using Knowledge of Human-Generated Code to Bias the Search in Program Synthesis with Grammatical Evolution (Page 331)
Dirk Schweim (University of Mainz), Erik Hemberg (MIT), Dominik Sobania (University of Mainz), Una-May O'Reilly (MIT), and Franz Rothlauf (University of Mainz)


Theory (back to top)

On the Effectiveness of Restarting Local Search (Page 333)
Aldeida Aleti and Mark Wallace (Monash University) and Markus Wagner (The University of Adelaide)

Affine OneMax (Page 335)
Arnaud Berny (Independent researcher)

Time Complexity Analysis of the Deductive Sort in the Best Case (Page 337)
Sumit Mishra and Ved Prakash (Indian Institute of Information Technology Guwahati)


Tutorial (back to top)


Introductory Tutorials (back to top)

Benchmarking: state-of-the-art and beyond (Page 339)
Anne Auger and Nikolaus Hansen (Inria)

Recent Advances in Particle Swarm Optimization Analysis and Understanding 2021 (Page 341)
Christopher W. Cleghorn (University of the Witwatersrand) and Andries P. Engelbrecht (University of Stellenbosch)

A Gentle Introduction to Theory (for Non-Theoreticians) (Page 369)
Benjamin Doerr (Ecole Polytechnique, Laboratoire d'Informatique (LIX))

Runtime Analysis of Evolutionary Algorithms: Basic Introduction (Page 399)
Per Kristian Lehre (University of Birmingham) and Pietro Simone Oliveto (The University of Sheffield)

Evolution of Neural Networks (Page 426)
Risto Miikkulainen (The University of Texas at Austin, Cognizant AI Labs)

Genetic Programming A Tutorial Introduction (Page 443)
Una-May O'Reilly (MIT CSAIL) and Erik Hemberg (MIT CSAIL, ALFA Group)

Replicability and Reproducibility in Evolutionary Optimization (Page 454)
Luís Paquete (University of Coimbra) and Manuel López-Ibáñez (University of Málaga)

Representations for Evolutionary Algorithms (Page 463)
Franz Rothlauf (University of Mainz)

Introductory Mathematical Programming for EC (Page 484)
Ofer M. Shir (Tel-Hai College, The Galilee Research Institute - Migal)

Learning Classifier Systems: From Principles to Modern Systems (Page 498)
Anthony Stein (University of Hohenheim) and Masaya Nakata (Yokohama National University)

Hyper-heuristics (Page 528)
Daniel R. Tauritz (Auburn University) and John R. Woodward (Queen Mary University of London)

Model-Based Evolutionary Algorithms (Page 558)
Dirk Thierens (University of Utrecht) and Peter A.N. Bosman (Centrum Wiskunde & Informatica (CWI))

Theoretical Foundations of Evolutionary Computation for Beginners and Veterans (Page 588)
Darrell Darrell Whitley (Colorado State University)


Advanced Tutorials (back to top)

CMA-ES and Advanced Adaptation Mechanisms (Page 636)
Youhei Akimoto (University of Tsukuba, RIKEN AIP) and Nikolaus Hansen (Inria, Ecole Polytechnique)

Benchmarking Multiobjective Optimizers 2.0 (Page 664)
Dimo Brockhoff (Inria; CMAP, Ecole Polytechnique, IP Paris) and Tea Tušar (Jozef Stefan Institute)

Advanced Learning Classifier Systems (Page 669)
Will Neil Browne (Queensland University of Technology)

Constraint-Handling Techniques used with Evolutionary Algorithms (Page 692)
Carlos Artemio Coello Coello (CINVESTAV-IPN)

Quality-Diversity Optimization (Page 715)
Antoine Cully (Imperial College); Jean-Baptiste Mouret (Inria); and Stéphane Doncieux (ISIR, Sorbonne Université)

Evolutionary Multi- and Many-Objective Optimization: Methodologies, Applications and Demonstration (Page 740)
Kalyanmoy Deb and Julian Blank (Michigan State University)

Statistical Analyses for Meta-heuristic Stochastic Optimization Algorithms (Page 770)
Tome Eftimov and Peter Korošec (Jožef Stefan Institute)

Genetic improvement: Taking real-world source code and improving it using computational search methods. (Page 786)
Saemundur Oskar Haraldsson (University of Stirling), John R. Woodward (Queen Mary University of London), Markus Wagner (The University of Adelaide), Alexander E.I. Brownlee (University of Stirling), and Bradley Alexander (The University of Adelaide)

Dynamic Multi-objective Optimization: Introduction, Challenges, Applications and Future Directions (Page 818)
Marde Helbig (Griffith University)

Lexicase Selection (Page 839)
Thomas Helmuth (Hamilton College) and William La Cava (University of Pennsylvania)

Runtime Analysis of Population-based Evolutionary Algorithms (Page 856)
Per Kristian Lehre (University of Birmingham) and Pietro Simone Oliveto (The University of Sheffield)

Decomposition Multi-Objective Optimization: Current Developments and Future Opportunities (Page 881)
Ke Li (University of Exeter) and Qingfu Zhang (City University of Hong Kong)

Recent Advances in Landscape Analysis for Optimisation and Learning (Page 899)
Katherine Mary Malan (University of South Africa) and Gabriela Ochoa (University of Stirling)

Evolutionary Submodular Optimisation (Page 918)
Aneta Neumann and Frank Neumann (The University of Adelaide) and Chao Qian (Nanjing University)

Sequential Experimentation by Evolutionary Algorithms (Page 941)
Ofer M. Shir (Tel-Hai College, The Galilee Research Institute - Migal) and Thomas Bäck (Leiden University)

Automated Algorithm Configuration and Design (Page 959)
Thomas Stützle (Université Libre de Bruxelles) and Manuel López-Ibáñez (University of Málaga, University of Manchester)

Coevolutionary Computation for Adversarial Deep Learning (Page 983)
Jamal Toutouh (Massachusetts Inst. of Technology, University of Málaga) and Una-May O’Reilly (Massachusetts Inst. of Technology)


Specialized Tutorials (back to top)

Evolutionary
 Art and Design: Representation, Fitness and Interaction (Page 1002)
Penousal Machado (University of Coimbra, CISUC)

Search Based Software Engineering: challenges, opportunities and recent applications (Page 1032)
Ali Ouni (Ecole de Technologie Superieure) and Mohamed Wiem Mkaouer (Rochester Institute of Technology)

Applications of Dynamic Parameter Control in Evolutionary Computation (Page 1064)
Gregor Papa (Jožef Stefan Institute)

Evolutionary Computation and Machine Learning in Cryptology (Page 1089)
Stjepan Picek (Delft University of Technology) and Domagoj Jakobovic (za)

Push (Page 1119)
Lee Spector (Hampshire College / Amherst College, University of Massachusetts Amherst)

Towards a Green AI: Evolutionary solutions for an ecologically viable artificial intelligence (Page 1135)
Nayat Sánchez-Pi and Luis Martí (Inria Chile Research Center)

Evolutionary Computation for Feature Selection and Feature Construction (Page 1141)
Bing XUE and Mengjie Zhang (Victoria University of Wellington)

Evolutionary Computation and Evolutionary Deep Learning for Image Analysis, Signal Processing and Pattern Recognition (Page 1169)
Mengjie Zhang (Victoria University of Wellington) and Stefano Cagnoni (University of Parma)


Workshop Paper (back to top)


Workshop Analysing Algorithmic Behaviour of Optimisation Heuristics (back to top)

Quantifying the Impact of Boundary Constraint Handling Methods on Differential Evolution (Page 1199)
Rick Boks, Anna Kononova, and Hao Wang (Leiden University)

On the Genotype Compression and Expansion for Evolutionary Algorithms in the Continuous Domain (Page 1208)
Lucija Planinić and Marko Đurasević (Faculty of Electrical Engineering and Computing), Luca Mariot (Delft University of Technology), Domagoj Jakobović (Faculty of Electrical Engineering and Computing), Stjepan Picek (Delft University of Technology), and Carlos Coello Coello (CINVESTAV-IPN)

Design of Large-Scale Metaheuristic Component Studies (Page 1217)
Helena Stegherr, Michael Heider, Leopold Luley, and Jörg Hähner (Universität Augsburg)
Additional Supplemental Material

Benchmark Generator for TD Mk Landscapes (Page 1227)
Tobias van Driessel and Dirk Thierens (Utrecht University)

Emergence of Structural Bias in Differential Evolution (Page 1234)
Bas van Stein (Leiden University), Fabio Caraffini (De Montfort University), and Anna Kononova (Leiden University)

Is there Anisotropy in Structural Bias? (Page 1243)
Diederick L. Vermetten and Anna V. Kononova (Leiden University), Fabio Caraffini (De Montfort University), and Hao Wang and Thomas Bäck (Leiden University)


Workshop Black Box Optimization Benchmarking (back to top)

DMS and MultiGLODS: Black-Box Optimization Benchmarking of Two Direct Search Methods on the Biobjective bbob-biobj Test Suite (Page 1251)
Dimo Brockhoff (Inria, IP Paris); Baptiste Plaquevent-Jourdain (ENSTA Paris); and Anne Auger and Nikolaus Hansen (Inria, IP Paris)

Benchmarking SHADE algorithm enhanced with model based optimization on the BBOB noiseless testbed (Page 1259)
Michał Okulewicz and Mateusz Zaborski (Faculty of Mathematics and Information Science Warsaw University of Technology)
Additional Supplemental Material


Workshop Decomposition Techniques in Evolutionary Optimization (back to top)

An Abstract Interface for Large-Scale Continuous Optimization Decomposition Methods (Page 1267)
Rodolfo Ayala Lopes, Rodrigo Silva, and Alan Freitas (Universidade Federal de Ouro Preto)

The Bee-Benders Hybrid Algorithm with application to Transmission Expansion Planning (Page 1275)
Cameron A.G. MacRae and Melih Ozlen (RMIT University) and Andreas Tilman Ernst (Monash University)

Population-Based Coordinate Descent Algorithm with Majority Voting (Page 1283)
Davood Zaman Farsa, Azam Asilian Bidgoli, Ehsan Rokhsatyazdi, and Shahryar Rahnamayan (University of Ontario Institute of Technology)


Workshop Evolutionary Algorithms and HPC (back to top)

An Efficient Fault-tolerant Communication Algorithm for Population-based Metaheuristics (Page 1290)
Amanda S. Dufek (Lawrence Berkeley National Laboratory), Douglas A. Augusto (Oswaldo Cruz Foundation), Helio J. C. Barbosa (National Laboratory for Scientific Computing), Pedro L. S. Dias (University of São Paulo - IAG), and Jack R. Deslippe (Lawrence Berkeley National Laboratory)

Improving the Scalability of Distributed Neuroevolution Using Modular Congruence Class Generated Innovation Numbers (Page 1299)
Joshua Karns and Travis Desell (Rochester Institute of Technology)

Generating Combinations on the GPU and its Application to the K-Subset Sum (Page 1308)
Victor Parque (Waseda University)

X-Aevol: GPU Implementation of an Evolutionary Experimentation Simulator (Page 1317)
Laurent Turpin, Jonathan Rouzaud-Cornabas, and Thierry Gautier (Inria)


Workshop Evolutionary Algorithms for Problems with Uncertainty (back to top)

Maximising Hypervolume and Minimising $\epsilon$-Indicators using Bayesian Optimisation over Sets (Page 1326)
Tinkle Chugh (University of Exeter) and Manuel López-Ibáñez (University of Málaga, University of Manchester)

RARE: Evolutionary Feature Engineering for Rare-variant Bin Discovery (Page 1335)
Satvik Dasariraju (University of Pennsylvania, The Lawrenceville School) and Ryan J. Urbanowicz (University of Pennsylvania)

A New Acquisition Function for Robust Bayesian Optimization of Unconstrained Problems (Page 1344)
Sibghat Ullah and Hao Wang (Leiden University), Stefan Menzel and Bernhard Sendhoff (Honda Research Institute Europe GmbH), and Thomas Bäck (Leiden University)


Workshop Evolutionary Computation and Decision Making (back to top)

A Divide and Conquer Approach for Web Services Location Allocation Problem (Page 1346)
Harshal Tupsamudre, Saket Saurabh, Arun Ramamurthy, Mangesh Gharote, and Sachin Lodha (TCS Research and Innovation, Tata Consultancy Services, India)

Model Learning with Personalized Interpretability Estimation (ML-PIE) (Page 1355)
Marco Virgolin (Chalmers University of Technology), Andrea De Lorenzo (University of Trieste), Francesca Randone (IMT School for Advanced Studies Lucca), Eric Medvet (University of Trieste), and Mattias Wahde (Chalmers University of Technology)


Workshop Evolutionary Computation for the Automated Design of Algorithms (back to top)

Towards Large Scale Automated Algorithm Designby Integrating Modular Benchmarking Frameworks (Page 1365)
Amine Aziz-Alaoui (IRT Saint Exupéry); Carola Doerr (CNRS, Sorbonne Université); and Johann Dreo (Pasteur Institute)

Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules (Page 1375)
Jacob de Nobel, Diederick Vermetten, and Hao Wang (Leiden University); Carola Doerr (Sorbonne Université, CNRS); and Thomas Baeck (Leiden University)

Automated Design of Accurate and Robust Image Classifiers with Brain Programming (Page 1385)
Gerardo Ibarra-Vazquez (Estudiante UASLP), Gustavo Olague (CICESE), Cesar Puente (UASLP), Mariana Chan-Ley (CICESE), and Carlos Soubervielle-Montalvo (UASLP)

A selection hyperheuristic guided by Thompson Sampling for numerical optimization (Page 1394)
Marcella Scoczynski (Federal University of Technology - Parana), Diego Oliva (Universidad de Guadalajara), Erick Rodriguez-Esparza (University of Deusto), Myriam Delgado and Ricardo Lüders (Federal University of Technology - Parana), Mohamed El Yafrani (Aalborg University), Luiz Ledo (Federal University of Technology - Parana), Mohamed Abd Elaziz (Zagazig University), and Marco Perez-Cisnero (Universidad de Guadalajara)

Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network for Molecular Property Prediction (Page 1403)
Yingfang Yuan, Wenjun Wang, and Wei Pang (Heriot-Watt University)


Workshop Evolutionary Computation for Permutation Problems (back to top)

Towards the Landscape Rotation as a Perturbation Strategy on the Quadratic Assignment Problem (Page 1405)
Joan Alza and Mark Bartlett (Robert Gordon University), Josu Ceberio (University of the Basque Country), and John McCall (Robert Gordon University)
Additional Supplemental Material

On the symmetry of the Quadratic Assignment Problem through Elementary Landscape Decomposition (Page 1414)
Xabier Benavides (Basque Center for Applied Mathematics) and Josu Ceberio and Leticia Hernando (University of the Basque Country)
Additional Supplemental Material

Generating Instances with Performance Differences for More Than Just Two Algorithms (Page 1423)
Jakob Bossek (University of Münster) and Markus Wagner (The University of Adelaide)

Exploratory Analysis Of The Monte Carlo Tree Search For Solving The Linear Ordering Problem (Page 1433)
Andoni Irazusta Garmendia, Josu Ceberio, and Alexander Mendiburu (University of the Basque Country)

Hybrid Linkage Learning for Permutation Optimization with Gene-pool Optimal Mixing Evolutionary Algorithms (Page 1442)
Michal Witold Przewozniczek and Marcin Michal Komarnicki (Wroclaw University of Science and Technology), Peter A.N. Bosman (Centrum Wiskunde & Informatica (CWI)), Dirk Thierens (Utrecht University), Bartosz Frej (Wroclaw University of Science and Technology), and Ngoc Hoang Luong (University of Information Technology)

An Empirical Evaluation of Permutation-Based Policies for Stochastic RCPSP (Page 1451)
Olivier Regnier-Coudert and Guillaume Povéda (Airbus)

Solving Job Shop Scheduling Problems Without Using a Bias for Good Solutions (Page 1459)
Thomas Weise, Xinlu Li, Yan Chen, and Zhize Wu (Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University)


Workshop Evolutionary Data Mining and Optimization over Graphs (back to top)

Graph-Aware Evolutionary Algorithms for Influence Maximization (Page 1467)
Kateryna Konotopska and Giovanni Iacca (University of Trento)

Focusing on the Hybrid Quantum Computing - Tabu Search Algorithm: new results on the Asymmetric Salesman Problem (Page 1476)
Eneko Osaba, Esther Villar-Rodriguez, and Izaskun Oregi (Tecnalia Research & Innovation) and Aitor Moreno-Fernandez-de-Leceta (Instituto Ibermatica de Innovacion)


Workshop Evolutionary Reinforcement Learning (back to top)

Novelty and MCTS (Page 1483)
Hendrik Baier and Michael Kaisers (Centrum Wiskunde & Informatica (CWI))

Using Deep Q-Network for Selection Hyper-Heuristics (Page 1488)
Augusto Dantas, Alexander Fiabane do Rego, and Aurora Pozo (Federal University of Paraná)

Coordinate Ascent MORE With Adaptive Entropy Control for Population-Based Regret Minimization (Page 1493)
Maximilian Hüttenrauch and Gerhard Neumann (Karlsruhe Institute of Technology)

On the challenges of jointly optimising robot morphology and control using a hierarchical optimisation scheme (Page 1498)
Léni Kenneth Le Goff and Emma Hart (Edinburgh Napier University)

Using Reinforcement Learning for Tuning Genetic Algorithms (Page 1503)
Jose Quevedo and Marwan Abdelatti (University of Rhode Island), Farhad Imani (University of Connecticut), and Manbir Sodhi (University of Rhode Island)

Evolutionary Reinforcement Learning for Sparse Rewards (Page 1508)
Shibei Zhu (Aalto university) and Francesco Belardinelli and Borja González León (Imperial College)
Additional Supplemental Material


Workshop Evolutionary Computation Software Systems (back to top)

AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms (Page 1513)
Kory Becker (Bloomberg) and Justin Gottschlich (Intel Corporation)

Paradiseo: From a Modular Framework for Evolutionary Computation to the Automated Design of Metaheuristics (Page 1522)
Johann Dreo (Pasteur Institute); Arnaud Liefooghe (Univ. Lille, CNRS); Sébastien Verel (Université du Littoral Côte d'Opale); Marc Schoenauer (Inria Saclay–Île-de-France, CNRS); Juan J. Merelo (University of Granada); Alexandre Quemy (Poznan University of Technology); Benjamin Bouvier (N/A); and Jan Gmys (Inria Lille - Nord Europe)

Component-Based Design of Multi-Objective Evolutionary Algorithms Using the Tigon Optimization Library (Page 1531)
Joao A. Duro, Daniel C. Oara, Ambuj K. Sriwastava, and Yiming Yan (The University of Sheffield); Shaul Salomon (ORT Braude College of Engineering); and Robin C. Purshouse (The University of Sheffield)

EBIC.JL - an Efficient Implementation of Evolutionary Biclustering Algorithm in Julia (Page 1540)
Paweł Renc (AGH University of Science and Technology); Patryk Orzechowski (University of Pennsylvania, AGH University of Science and Technology); Jarosław Wąs and Aleksander Byrski (AGH University of Science and Technology); and Jason H. Moore (University of Penn)


Workshop Industrial Applications of Metaheuristics (back to top)

A Multi-Objective Genetic Algorithm for Jacket Optimization (Page 1549)
Jan Burak (Norwegian University of Science and Technology) and Ole Jakob Mengshoel (Norwegian University of Science and Technology, Carnegie Mellon University)

Using Grammatical Evolution for Modelling Energy Consumption on a Computer Numerical Control Machine (Page 1557)
Samuel Carvalho (Limerick Institute of Technology, University of Limerick); Joe Sullivan (Limerick Institute of Technology); and Douglas Dias, Enrique Naredo, and Conor Ryan (University of Limerick)

A heuristic approach to feasibility verification for truck loading (Page 1564)
Vinicius Gandra, Hatice Çalik, Tony Wauters, and Greet Vanden Berghe (KU Leuven)

Trustworthy AI for Process Automation on a Chylla-Haase Polymerization Reactor (Page 1570)
Daniel Hein and Daniel Labisch (Siemens AG)

Multi Tree Operators for Genetic Programming to Identify Optimal Energy Flow Controllers (Page 1579)
Kathrin Kefer (Fronius International GmbH), Roland Hanghofer (Dynatrace Austria GmbH), Patrick Kefer (University of Applied Sciences Upper Austria), Markus Stöger and Bernd Hofer (Fronius International GmbH), and Michael Affenzeller and Stephan Winkler (University of Applied Sciences Upper Austria)

Simulation-based Scheduling of a Large-scale Industrial Formulation Plant Using a Heuristics-assisted Genetic Algorithm (Page 1587)
Christian Klanke (TU Dortmund); Dominik Bleidorn, Christian Koslowski, and Christian Sonntag (INOSIM Software GmbH); and Sebastian Engell (TU Dortmund)

Addressing the Multiplicity of Solutions in Optical Lens Design as a Niching Evolutionary Algorithms Computational Challenge (Page 1596)
Anna V. Kononova (Leiden University); Ofer M. Shir (Computer Science Department, Tel-Hai College, and Migal Institute); Teus Tukker and Pierluigi Frisco (ASML); and Shutong Zeng and Thomas Bäck (Leiden University)

Advanced Mine Optimisation under Uncertainty Using Evolution (Page 1605)
William Reid (Maptek Pty. Ltd), Aneta Neumann (The University of Adelaide), Simon Ratcliffe (Maptek Pty. Ltd), and Frank Neumann (The University of Adelaide)

Determining a consistent experimental setup for benchmarking and optimizing databases (Page 1614)
Moisés Silva-Muñoz (IRIDIA-CoDE, Université Libre de Bruxelles); Gonzalo Calderon (CeDInt-UPM, Universidad Politecnica de Madrid); and Alberto Franzin and Hugues Bersini (IRIDIA-CoDE, Université Libre de Bruxelles)

Multi-Objective Evolutionary Product Bundling: A Case Study (Page 1622)
Okan Tunalı and Ahmet Tuğrul Bayrak (Ata Technology Platforms), Víctor Sanchez-Anguix (Universitat Politècnica de València), and Reyhan Aydoğan (Özyeğin University)


Workshop International Workshop on Learning Classifier Systems (back to top)

A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning Policies (Page 1630)
Jordan T. Bishop and Marcus Gallagher (University of Queensland) and Will N. Browne (Queensland University of Technology)

An Experimental Comparison of Explore/Exploit Strategies for the Learning Classifier System XCS (Page 1639)
Tim Hansmeier and Marco Platzner (Paderborn University)

An Overview of LCS Research from 2020 to 2021 (Page 1648)
David Pätzel and Michael Heider (University of Augsburg) and Alexander R. M. Wagner (University of Hohenheim)


Workshop Landscape-Aware Heuristic Search (back to top)

Understanding Parameter Spaces using Local Optima Networks: A Case Study on Particle Swarm Optimization (Page 1657)
Christopher W. Cleghorn (The University of the Witwatersrand) and Gabriela Ochoa (University of Stirling)

Investigating the Landscape of a Hybrid Local Search Approach for a Timetabling Problem (Page 1665)
Thomas Feutrier, Marie-Éléonore Kessaci, and Nadarajen Veerapen (Universite de Lille)

Towards Population-based Fitness Landscape Analysis Using Local Optima Networks (Page 1674)
Melike Dila Karatas, Ozgur Ekim Akman, and Jonathan Edward Fieldsend (University of Exeter)

Dissipative Polynomials (Page 1683)
William B. Langdon (ucl) and Justyna Petke and David Clark (University College London)

Analysing the Loss Landscape Features of Generative Adversarial Networks (Page 1692)
Jarrod Moses (University of Pretoria), Katherine Malan (University of South Africa), and Anna Bosman (University of Pretoria)

Dynamic Landscape Analysis for Open-Ended Stacking (Page 1700)
Bernhard Werth (University of Applied Sciences Upper Austria, Johannes Keppler University Linz) and Johannes Karder, Andreas Beham, and Stefan Wagner (University of Applied Sciences Upper Austria)


Workshop Neuroevolution at Work (back to top)

Prediction of Personalized Blood Glucose Levels in Type 1 Diabetic Patients using a Neuroevolution Approach (Page 1708)
Ivanoe De Falco (ICAR-CNR), Antonio Della Cioppa and Angelo Marcelli (University of Salerno), Umberto Scafuri (ICAR-CNR), Luca Stellaccio (University of Salerno), and Ernesto Tarantino (ICAR-CNR)

Evolving Neural Selection with Adaptive Regularization (Page 1717)
Li Ding (University of Massachusetts Amherst, Massachusetts Institute of Technology) and Lee Spector (Amherst College, Hampshire College)

Pareto-Optimal Progressive Neural Architecture Search (Page 1726)
Eugenio Lomurno, Stefano Samele, Matteo Matteucci, and Danilo Ardagna (Politecnico di Milano)

Neuroevolution of Recurrent Neural Networks for Time Series Forecasting of Coal-Fired Power Plant Operating Parameters (Page 1735)
Zimeng Lyu (Rochester Institute of Technology); Shuchita Patwardhan, David Stadem, James Langfeld, Steve Benson, and Seth Thoelke (Microbeam Technologies Incorporated); and Travis Desell (Rochester Institute of Technology)

On the Effects of Pruning on Evolved Neural Controllers for Soft Robots (Page 1744)
Giorgia Nadizar (Department of Engineering and Architecture, University of Trieste; Department of Computer Science, Artificial Intelligence Lab, Oslo Metropolitan University); Eric Medvet, Felice Andrea Pellegrino, and Marco Zullich (Department of Engineering and Architecture, University of Trieste); and Stefano Nichele (Department of Computer Science, Artificial Intelligence Lab, Oslo Metropolitan University; Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering)
Additional Supplemental Material

Behavior-based Neuroevolutionary Training in Reinforcement Learning (Page 1753)
Jörg Stork, Martin Zaefferer, Nils Eisler, Patrick Tichelmann, and Thomas Bartz-Beielstein (TH Köln) and A. E. Eiben (VU Amsterdam)

Hybrid Encodings for Neuroevolution of Convolutional Neural Networks: A Case Study (Page 1762)
Gustavo-Adolfo Vargas-Hákim, Efrén Mezura-Montes, and Héctor-Gabriel Acosta-Mesa (Artificial Intelligence Research Institute, University of Veracruz)


Workshop Parallel and Distributed Evolutionary Inspired Methods (back to top)

A Partially Asynchronous Global Parallel Genetic Algorithm (Page 1771)
Darren M. Chitty (Aston University)

An operation to promote diversity in Evolutionary Algorithms in a Dynamic Hybrid Island Model (Page 1779)
Grasiele Duarte and Beatriz Lima (Federal University of Rio de Janeiro)
Additional Supplemental Material

Solving QUBO with GPU Parallel MOPSO (Page 1788)
Noriyuki Fujimoto and Kouki Nanai (Osaka Prefecture University)

Conduit: A C++ Library for Best-Effort High Performance Computing (Page 1795)
Matthew Andres Moreno, Santiago Rodriguez Papa, and Charles Ofria (Michigan State University)

Island Model in ActoDatA: an actor-based Implementation of a classical Distributed Evolutionary Computation Paradigm (Page 1801)
Giuseppe Petrosino, Federico Bergenti, Gianfranco Lombardo, Monica Mordonini, Agostino Poggi, Michele Tomaiuolo, and Stefano Cagnoni (University of Parma)


Workshop Real-World Applications of Continuous and Mixed-Integer Optimization (back to top)

House Price Prediction Using Clustering and Genetic Programming along with Conducting a Comparative Study (Page 1809)
Fateme Azimlu, Shahryar Rahnamayan, and Masoud Makrehchi (University of Ontario Institute of Technology)

A Matheuristic Approach for Finding Effective Base Locations and Team Configurations for North West Air Ambulance (Page 1817)
Burak Boyaci (Lancaster University); Muhammad Ali Nayeem (Lancaster University, Bangladesh University of Engineering and Technology); and Ahmed Kheiri (Lancaster University)

Project Portfolio Selection with Defense Capability Options (Page 1825)
Kyle Robert Harrison, Saber Elsayed, and Ruhul A. Sarker (University of New South Wales) and Ivan L. Garanovich, Terence Weir, and Sharon G. Boswell (Defence Science and Technology Group, Department of Defence)

Black-box adversarial attacks using Evolution Strategies (Page 1827)
Hao Qiu, Leonardo Lucio Custode, and Giovanni Iacca (University of Trento)

Evolutionary Algorithms in High-dimensional Radio Access Network Optimization (Page 1834)
Dmitriy Semenchikov (Saint Petersburg State University; HUAWEI, St. Petersburg Research Center); Anna Filippova (HUAWEI, St. Petersburg Research Center); Dmitriy Volf and Nikolai Kovrizhnykh (Saint Petersburg State University; HUAWEI, St. Petersburg Research Center); Maxim Mironov (Moscow Institute of Physics and Technology; HUAWEI, St. Petersburg Research Center); Zou Jinying (Saint Petersburg State University; HUAWEI, St. Petersburg Research Center); Luo Ronghui and Zhu Yuanming (HUAWEI, China); and Huang Wei and Chai Dapeng (HUAWEI, St. Petersburg Research Center)


Workshop Surrogate-Assisted Evolutionary Optimisation (back to top)

Preferential Bayesian optimisation with Skew Gaussian Processes (Page 1842)
Alessio Benavoli (Trinity College Dubin) and Dario Azzimonti and Dario Piga (IDSIA Dalle Molle Institute for Artificial Intelligence Research)
Additional Supplemental Material

Black-box Mixed-Variable Optimisation Using a Surrogate Model that Satisfies Integer Constraints (Page 1851)
Laurens Bliek (Technical University of Eindhoven); Arthur Guijt (Centrum Wiskunde & Informatica (CWI), Delft University of Technology); and Sicco Verwer and Mathijs de Weerdt (Delft University of Technology)
Additional Supplemental Material

How Bayesian Should Bayesian Optimisation Be? (Page 1860)
George De Ath, Richard M. Everson, and Jonathan E. Fieldsend (University of Exeter)
Additional Supplemental Material

A Two-Phase Surrogate Approach for High-Dimensional Constrained Discrete Multi-Objective Optimization (Page 1870)
Rommel G. Regis (Saint Joseph's University, Mathematics Department)

Augmenting High-dimensional Nonlinear Optimization with Conditional GANs (Page 1879)
Pouya Rezazadeh Kalehbasti, Michael David Lepech, and Samarpreet Singh Pandher (Stanford University)
Additional Supplemental Material


Workshop Genetic and Evolutionary Computation in Defense, Security, and Risk Management (back to top)

Multi-objective Evolutionary Algorithms for Distributed Tactical Control of Heterogeneous Agents (Page 1881)
Rahul Dubey and Sushil J. Louis (University of Nevada Reno)

Deceiving Neural Source Code Classifiers: Finding Adversarial Examples with Grammatical Evolution (Page 1889)
Claudio Ferretti and Martina Saletta (Università degli Studi di Milano-Bicocca)

Competitive Coevolution for Defense and Security: Elo-Based Similar-Strength Opponent Sampling (Page 1898)
Sean N. Harris and Daniel R. Tauritz (Auburn University)

Simulating a Logistics Enterprise Using an Asymmetrical Wargame Simulation with Soar Reinforcement Learning and Coevolutionary Algorithms (Page 1907)
Ying Zhao (Naval Postgraduate School); Erik Hemberg (Massachusetts Institute of Technology, CSAIL); Nate Derbinsky (Northeastern University); Gabino Mata (US Marine Corp.); and Una-May O’Reilly (Massachusetts Institute of Technology, CSAIL)


Workshop Swarm Intelligence Algorithms: Foundations, Perspectives and Challenges (back to top)

Self-organizing Migrating Algorithm with Clustering-aided Migration and Adaptive Perturbation Vector Control (Page 1916)
Tomas Kadavy, Michal Pluhacek, Adam Viktorin, and Roman Senkerik (Tomas Bata University in Zlin)
Additional Supplemental Material

A Population-based Automatic Clustering Algorithm for Image Segmentation (Page 1931)
Seyed Jalaleddin Mousavirad (Hakim Sabzevari University); Gerald Schaefer (Loughborough University); Mahshid Helali Moghadam (Malardalen University, RISE Research Institutes of Sweden); Mehrdad Saadatmand (RISE Research Institutes of Sweden); and Mahdi Pedram (Lorestan University of Medical Sciences)

HCS-BBD: An Effective Population-Based Approach for Multi-Level Thresholding (Page 1923)
Seyed Jalaleddin Mousavirad (Hakim Sabzevari University), Gerald Schaefer (Loughborough University), and Diego Oliva and Salvador Hinojosa (Universidad de Guadalajara)

A Differential Particle Scheme and its Application to PID Parameter Tuning of an Inverted Pendulum (Page 1937)
Victor Parque (Waseda University)

Explaining SOMA: the Relation of Stochastic Perturbation to Population Diversity and Parameter Space Coverage (Page 1944)
Michal Pluhacek, Anezka Kazikova, Tomas Kadavy, Adam Viktorin, and Roman Senkerik (Tomas Bata University in Zlin)


Workshop Visualisation Methods in Genetic and Evolutionary Computation (back to top)

Visualizing fitnesses and constraint violations in single-objective optimization (Page 1953)
Tomofumi KITAMURA and Alex FUKUNAGA (The University of Tokyo)
Additional Supplemental Material

Many-objective Population Visualisation with Geons (Page 1961)
Marius Nicolae Varga, Swen Gaudl, and David Walker (University of Plymouth)


Student Workshop (back to top)

CLAHC - Custom Late Acceptance Hill Climbing: first results on TSP (Page 1970)
Sylvain Clay, Lucien Mousin, Nadarajen Veerapen, and Laetitia Jourdan (Université de Lille)

Negative Learning Ant Colony Optimization for the Minimum Positive Influence Dominating Set Problem (Page 1974)
Albert López Serrano (Universitat Autònoma de Barcelona), Teddy Nurcahyadi (IIIA-CSIC), Salim Bouamama (Ferhat Abbas University), and Christian Blum (IIIA-CSIC)

A Parallel Genetic Algorithm to Speed Up the Resolution of the Algorithm Selection Problem (Page 1978)
Alejandro Marrero, Eduardo Segredo, and Coromoto Leon (Universidad de La Laguna)

Automated Parameter Choice with Exploratory Landscape Analysis and Machine Learning (Page 1982)
Maxim Pikalov and Vladimir Mironovich (ITMO University)

The Lower Bounds on the Runtime of the $(1 + (\lambda, \lambda))$~GA on the Minimum Spanning Tree Problem (Page 1986)
Matvey Shnytkin and Denis Antipov (ITMO University)

Concurrent Neural Tree and Data Preprocessing AutoML for Image Classification (Page 1990)
Anish Thite, Mohan Ashish Dodda, Alex Liu, and Pulak Agarwal (Georgia Institute of Technology) and Jason Zutty (Georgia Tech Research Institute)

Population network structure impacts genetic algorithm optimisation performance (Page 1994)
Aymeric Vié (University of Oxford)