Papers


Track: Ant Colony Optimization and Swarm Intelligence (back to top)

Information Sharing and Conflict Resolution in Distributed Factored Evolutionary Algorithms (Page 5)
Stephyn Butcher (Johns Hopkins University) and John Sheppard and Shane Strasser (Montana State University)
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Using Ant Colony Optimization to Optimize Long Short-Term Memory Recurrent Neural Networks (Page 13)
AbdElRahman ElSaid, Fatima El Jamiy, James Higgins, Brandon Wild, and Travis Desell (University of North Dakota)
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A Model of Artificial Emotions for Behavior-Modulation and Implicit Coordination in Multi-robot Systems (Page 21)
Jerome Guzzi, Alessandro Giusti, and Luca Maria Gambardella (IDSIA Dalle Molle Institute for Artificial Intelligence Research) and Gianni A. Di Caro (Carnegie Mellon University - Qatar)
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Recurrent Neural Network-Predictions for PSO in Dynamic Optimization (Page 29)
Almuth Meier and Oliver Kramer (University of Oldenburg, Oldenburg, Germany)
Supplemental Material: pap309s3-file2.pdf


A Particle Swarm Optimization based Feature Selection Approach to Transfer Learning in Classification (Page 37)
Bach Hoai Nguyen, Bing Xue, and Peter Andreae (Victoria University of Wellington)
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Semi-supervised Learning Assisted Particle Swarm Optimization of Computationally Expensive Problems (Page 45)
Chaoli Sun (Taiyuan University of Science and Technology), Yaochu Jin (University of Surrey), and Ying Tan (Taiyuan University of Science and Technology)
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A New Foraging-Based Algorithm for Online Scheduling (Page 53)
Koen van der Blom and Thomas Bäck (Leiden University)
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Track: Complex Systems (Artificial Life/Artificial Immune Systems/Generative and Developmental Systems/Evolutionary Robotics/Evolvable Hardware) (back to top)

Towards the Targeted Environment-Specific Evolution of Robot Components (Page 61)
Jack Collins (CSIRO, Queensland University of Technology); Wade Geles and Gerard Howard (CSIRO); and Frederic Maire (Queensland University of Technology)
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Hierarchical Behavioral Repertoires with Unsupervised Descriptors (Page 69)
Antoine Cully and Yiannis Demiris (Imperial College)
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How swarm size during evolution impacts the behavior, generalizability, and brain complexity of animats performing a spatial navigation task (Page 77)
Dominik Fischer (Technische Universität München), Sanaz Mostaghim (University of Magdeburg), and Larissa Albantakis (University of Madison-Wisconsin)
Supplemental Material: pap723s3-file2.pdf


Data-efficient Neuroevolution with Kernel-Based Surrogate Models (Page 85)
Adam Gaier (Bonn-Rhein-Sieg University of Applied Sciences, Inria); Alexander Asteroth (Bonn-Rhein-Sieg University of Applied Sciences); and Jean-Baptiste Mouret (Inria)
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Fusing Novelty and Surprise for Evolving Robot Morphologies (Page 93)
Daniele Gravina, Antonios Liapis, and Georgios N. Yannakakis (University of Malta)
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Evolution of a Functionally Diverse Swarm via a Novel Decentralised QD Algorithm (Page 101)
Emma Hart, Andreas Siegfried Wilhelm Steyven, and Ben Paechter (Edinburgh Napier University)
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Interoceptive robustness through environment-mediated morphological development (Page 109)
Sam Kriegman (University of Vermont), Nick Cheney (University of Wyoming), Francesco Corucci (3DNextech s.r.l.), and Josh Bongard (University of Vermont)
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Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients (Page 117)
Joel Lehman, Jay Chen, Jeff Clune, and Kenneth O. Stanley (Uber AI Labs)
Supplemental Material: pap156s3-file2.pdf


Real-World Evolution Adapts Robot Morphology and Control to Hardware Limitations (Page 125)
Tønnes F. Nygaard, Charles Patrick Martin, Eivind Samuelsen, Jim Torresen, and Kyrre Glette (University of Oslo)
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Automatic Synthesis of Swarm Behavioural Rules from their Atomic Components (Page 133)
Dilini Samarasinghe, Erandi Lakshika, Michael Barlow, and Kathryn Kasmarik (The University of New South Wales)
Supplemental Material: pap373s3-file2.zip


On an Immuno-inspired Distributed, Embodied Action-Evolution cum Selection Algorithm (Page 141)
Tushar Semwal, Divya D. Kulkarni, and Shivashankar B. Nair (Indian Institute of Technology Guwahati)
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Discovering the Elite Hypervolume by Leveraging Interspecies Correlation (Page 149)
Vassilis Vassiliades and Jean-Baptiste Mouret (Inria)
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Evolution of Fin Undulation on a Physical Knifefish-inspired Soft Robot (Page 157)
Frank Veenstra, Jonas Jørgensen, and Sebastian Risi (IT University of Copenhagen)
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A Robot to Shape your Natural Plant: The Machine Learning Approach to Model and Control Bio-Hybrid Systems (Page 165)
Mostafa Wahby (University of Lübeck), Mary Katherine Heinrich (Royal Danish Academy), Daniel Nicolas Hofstadler and Payam Zahadat (University of Graz), Sebastian Risi (IT University of Copenhagen), Phil Ayres (Royal Danish Academy), Thomas Schmickl (University of Graz), and Heiko Hamann (University of Lübeck)
Supplemental Material: pap290s3-file2.mp4


Robotic Snake Simulation using Ensembles of Artificial Neural Networks in Evolutionary Robotics (Page 173)
Grant Warren Woodford and Mathys Cornelius du Plessis (Nelson Mandela University)
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Track: Digital Entertainment Technologies and Arts (back to top)

Generating Beginner Heuristics for Simple Texas Hold'em (Page 181)
Fernando De Mesentier Silva, Julian Togelius, Frank Lantz, and Andy Nealen (New York University)
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Opponent Modeling and Exploitation in Poker Using Evolved Recurrent Neural Networks (Page 189)
Xun Li and Risto Miikkulainen (The University of Texas at Austin)
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Generating a Melody Based on Symbiotic Evolution for Musicians' Creative Activities (Page 197)
Noriko Otani and Daisuke Okabe (Tokyo City University) and Masayuki Numao (Osaka University)
Supplemental Material: pap184s3-file2.zip


Evolving Indirectly Encoded Convolutional Neural Networks to Play Tetris With Low-Level Features (Page 205)
Jacob Schrum (Department of Mathematics and Computer Science, Southwestern University)
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Querying Across Time to Interactively Evolve Animations (Page 213)
Isabel Tweraser, Lauren E. Gillespie, and Jacob Schrum (Department of Mathematics and Computer Science, Southwestern University)
Supplemental Material: pap116s3-file2.zip


Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network (Page 221)
Vanessa Volz (TU Dortmund University); Jacob Schrum (Southwestern University); Jialin Liu (Queen Mary University of London); Simon M. Lucas (Queen); Adam M. Smith (University of California, Santa Cruz); and Sebastian Risi (IT University of Copenhagen)
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Evolving simple programs for playing Atari games (Page 229)
Dennis G. Wilson, Sylvain Cussat-Blanc, and Hervé Luga (University of Toulouse) and Julian F. Miller (University of York)
Supplemental Material: pap466s3-file2.zip



Track: Evolutionary Combinatorial Optimization and Metaheuristics (back to top)

Min-Conflicts Heuristic for Multi-Mode Resource-Constrained Projects Scheduling (Page 237)
Arben Ahmeti and Nysret Musliu (TU Wien)
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An effective hybrid meta-heuristic for a heterogeneous flow shop scheduling problem (Page 245)
Matheus de Freitas Araujo, Jose Elias Claudio Arroyo, and Ricardo Goncalves Tavares (Universidade Federal de Viçosa)
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Algorithm Selection on Generalized Quadratic Assignment Problem Landscapes (Page 253)
Andreas Beham, Stefan Wagner, and Michael Affenzeller (University of Applied Sciences Upper Austria, Johannes Kepler University)
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A Multi-objective Formulation of the Team Formation Problem in Social Networks: Preliminary Results (Page 261)
Carlos Brizuela and Julio Juarez (CICESE)
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Enhancing Partition Crossover with Articulation Points Analysis (Page 269)
Francisco Chicano (University of Malaga), Gabriela Ochoa (University of Stirling), Darrell Whitley (Colorado State University), and Renato Tinós (University of Sao Paulo)
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A fitness landscape analysis of the Travelling Thief Problem (Page 277)
Mohamed El Yafrani (Mohammed V University in Rabat), Marcella Scoczynski Ribeiro Martins (Federal University of Technology - Parana UTFPR), Mehdi El Krari (Mohammed V University in Rabat), Markus Wagner (The University of Adelaide), Myriam Delgado (Federal University of Technology - Parana UTFPR), Belaïd Ahiod (Mohammed V University in Rabat), and Ricardo Lüders (Federal University of Technology - Parana UTFPR)
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A heuristic algorithm based on Tabu Search for the solution of Flexible Job Shop Scheduling Problems with Lot Streaming (Page 285)
Miguel Ángel Fernández Romero and Eric Alfredo Rincón García (Universidad Autónoma Metropolitana - Azcapotzalco), Antonin Ponsich (Universidad Autonoma Metropolitana - Unidad Azcapotzalco), and Roman Anselmo Mora Gutiérrez (Universidad Autónoma Metropolitana - Azcapotzalco)
Supplemental Material: pap333s3-file2.pdf


Escaping Large Deceptive Basins of Attraction with Heavy-Tailed Mutation Operators (Page 293)
Tobias Friedrich and Francesco Quinzan (Hasso Plattner Institute) and Markus Wagner (The University of Adelaide)
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Improving the Run Time of the (1+1) Evolutionary Algorithm with Luby Sequences (Page 301)
Tobias Friedrich, Timo Kötzing, and Francesco Quinzan (Hasso Plattner Institute) and Andrew M. Sutton (University Minnesota Duluth)
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Randomized Greedy Algorithms for Covering Problems (Page 309)
Wanru Gao (The University of Adelaide), Tobias Friedrich (Hasso-Plattner-Institut), Frank Neumann (The University of Adelaide), and Christian Hercher (University of Flensburg)
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A Merge Search Algorithm and its Application to the Constrained Pit Problem in Mining (Page 316)
Angus Kenny and Xiaodong Li (RMIT University) and Andreas T. Ernst (Monash University)
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Dominance, Epsilon, and Hypervolume Local Optimal Sets in Multi-objective Optimization, and How to Tell the Difference (Page 324)
Arnaud Liefooghe (Univ. Lille, Inria Lille - Nord Europe); Manuel López-Ibáñez (University of Manchester); Luís Paquete (University of Coimbra); and Sébastien Verel (Université du Littoral Côte d'Opale)
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Evolutionary Multi-Level Acyclic Graph Partitioning (Page 332)
Orlando Moreira and Merten Popp (Intel Corporation) and Christian Schulz (University of Vienna)
Supplemental Material: pap132s3-file2.pdf


A two-level diploid genetic based algorithm for solving the family traveling salesman problem (Page 340)
Petrica Claudiu Pop, Oliviu Matei, and Camelia Pintea (Technical University of Cluj-Napoca)
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Memetic Multilevel Hypergraph Partitioning (Page 347)
Sebastian Schlag (Karlsruhe Institute of Technology), Christian Schulz (University of Vienna), and Robin Andre (Karlsruhe Institute of Technology)
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Fitness Landscape Analysis around the Optimum in Computational Protein Design (Page 355)
David Simoncini and Sophie Barbe (LISBP), Thomas Schiex (INRA), and Sébastien Verel (Université du Littoral Côte d'Opale)
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One-Class Constraint Acquisition with Local Search (Page 363)
Daniel Sroka and Tomasz P. Pawlak (Institute of Computing Science, Poznan University of Technology)
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Multifractality and Dimensional Determinism in Local Optima Networks (Page 371)
Sarah Louise Thomson (University of Stirling); Sébastien Verel (Université du Littoral Côte d'Opale); and Gabriela Ochoa, Nadarajen Veerapen, and David Cairns (University of Stirling)
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Iterated Greedy Algorithms for the Hybrid Flowshop Scheduling with Total Flow Time Minimization (Page 379)
Hande Öztop, M. Fatih Tasgetiren, and Deniz Türsel Eliiyi (Yasar University) and Quan-Ke Pan (Huazhong University of Science and Technology)
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Track: Evolutionary Machine Learning (back to top)

Combating catastrophic forgetting with developmental compression. (Page 386)
Shawn L. Beaulieu, Sam Kriegman, and Josh C. Bongard (University of Vermont)
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Optimizing Floating Centroids Method Neural Network Classifier Using Dynamic Multilayer Particle Swarm Optimization (Page 394)
Changwei Cai, Shuangrong Liu, Lin Wang, Bo Yang, Zhenxiang Chen, and Jin Zhou (University of Jinan)
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Autostacker: A Compositional Evolutionary Learning System (Page 402)
Boyuan Chen and Harvey Wu (Columbia University), Warren Mo and Ishanu Chattopadhyay (University of Chicago), and Hod Lipson (Columbia University)
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Neuroevolution of Hierarchical Reservoir Computers (Page 410)
Matthew Dale (University of York)
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Evolving Bagging Ensembles Using a Spatially-Structured Niching Method (Page 418)
Grant Dick, Caitlin A. Owen, and Peter A. Whigham (University of Otago, Information Science Dept.)
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Online Meta-Learning by Parallel Algorithm Competition (Page 426)
Stefan Elfwing and Eiji Uchibe (ATR) and Kenji Doya (OIST)
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Evolved GANs for generating Pareto set approximations (Page 434)
Unai Garciarena, Alexander Mendiburu, and Roberto Santana (University of the Basque Country)
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Evolutionary Expectation Maximization (Page 442)
Enrico Guiraud (University of Oldenburg, CERN) and Jakob Drefs and Jörg Lücke (University of Oldenburg)
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ES Is More Than Just a Traditional Finite-Difference Approximator (Page 450)
Joel Lehman and Jay Chen (Uber AI Labs); Jeff Clune (Uber AI Labs, University of Wyoming); and Kenneth O. Stanley (Uber AI Labs)
Supplemental Material: pap157s3-file2.pdf


Automatically Evolving Difficult Benchmark Feature Selection Datasets with Genetic Programming (Page 458)
Andrew Lensen, Bing Xue, and Mengjie Zhang (Victoria University of Wellington)
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Evolutionary Architecture Search for Deep Multitask Networks (Page 466)
Jason Zhi Liang, Elliot Meyerson, and Risto Miikkulainen (Sentient Technologies Inc., University of Texas at Austin)
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Divide and Conquer: Neuroevolution for Multiclass Classification (Page 474)
Tyler McDonnell (SparkCognition, Inc.; The University of Texas at Austin); Sari Andoni, Elmira Bonab, Sheila Cheng, Jun-Hwan Choi, Jimmie Goode, Keith Moore, and Gavin Sellers (SparkCognition, Inc.); and Jacob Schrum (SparkCognition, Inc.; Southwestern University)
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Theoretical Adaptation of Multiple Rule-Generation in XCS (Page 482)
Masaya Nakata (Yokohama National University), Will Neil Browne (Victoria University of Wellington), and Tomoki Hamagami (Yokohama National University)
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NEAT for Large-Scale Reinforcement Learning through Evolutionary Feature Learning and Policy Gradient Search (Page 490)
Yiming Peng, Gang Chen, Harman Singh, and Mengjie Zhang (Victoria University of Wellington)
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A Genetic Algorithm for Finding an Optimal Curing Strategy for Epidemic Spreading in Weighted Networks (Page 498)
Clara Pizzuti (Institute for High Performance Computing and Networking - ICAR National Research Council of Italy - CNR) and Annalisa Socievole (Institute for High Performance and Networking - ICAR National Research Council of Italy - CNR)
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Memetic Evolution of Deep Neural Networks (Page 505)
Pablo Ribalta Lorenzo (Future Processing, Silesian University of Technology) and Jakub Nalepa (Silesian University of Technology, Future Processing)
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Cooperative Multi-Objective Evolutionary Support Vector Machines for Multiclass Problems (Page 513)
Alejandro Rosales-Pérez and Andres Eduardo Gutierrez-Rodríguez (Tecnologico de Monterrey), Salvador García (University of Granada), Hugo Terashima-Marín (Tecnologico de Monterrey), Carlos A. Coello Coello (CINVESTAV-IPN), and Francisco Herrera (University of Granada)
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Towards an Adaptive Encoding for Evolutionary Data Clustering (Page 521)
Cameron Shand, Richard Allmendinger, Julia Handl, and John Keane (University of Manchester)
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CovSel: A New Approach for Ensemble Selection Applied to Symbolic Regression Problems (Page 529)
Dominik Sobania and Franz Rothlauf (University of Mainz)
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What About Interpolation? A Radial Basis Function Approach to Classifier Prediction Modeling in XCSF (Page 537)
Anthony Stein, Simon Menssen, and Jörg Hähner (University of Augsburg)
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Efficient Sample Reuse in Policy Search by Multiple Importance Sampling (Page 545)
Eiji Uchibe (Advanced Telecommunications Research Institute International)
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Attribute Tracking: Strategies Towards Improved Detection and Characterization of Complex Associations (Page 553)
Ryan John Urbanowicz, Christopher Lo, John Holmes, and Jason Moore (University of Pennsylvania)
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Ensembles of Evolved Nested Dichotomies for Classification (Page 561)
Marcel Wever, Felix Mohr, and Eyke Hüllermeier (Paderborn University)
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Limited Evaluation Cooperative Co-evolutionary Differential Evolution for Large-scale Neuroevolution (Page 569)
Anil Yaman and Decebal Constantin Mocanu (Technical University of Eindhoven), Giovanni Iacca (University of Trento), and George Fletcher and Mykola Pechenizkiy (Technical University of Eindhoven)
Supplemental Material: pap412s3-file2.pdf


Evolutionary Feature Subspaces Generation for Ensemble Classification (Page 577)
Boyu Zhang, A. K. Qin, and Timos Sellis (Swinburne University of Technology)
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Track: Evolutionary Multiobjective Optimization (back to top)

Local Search Effects in Bi-Objective Orienteering (Page 585)
Jakob Bossek, Christian Grimme, and Stephan Meisel (University of Münster); Günter Rudolph (TU Dortmund University); and Heike Trautmann and Heike Trautmann (University of Münster)
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Generalized Offline Orthant Search: One Code for Many Problems in Multiobjective Optimization (Page 593)
Maxim Buzdalov (ITMO University)
Supplemental Material: pap144s3-file2.pdf


A Differential Prediction Model for Evolutionary Dynamic Multiobjective Optimization (Page 601)
Leilei Cao and Lihong Xu (Tongji University), Erik D. Goodman (Michigan State University), Shuwei Zhu (Tongji University), and Hui Li (Xi'an Jiaotong University)
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Surrogate-assisted Evolutionary Biobjective Optimization for Objectives with Non-uniform Latencies (Page 609)
Tinkle Chugh (University of Exeter, UK; University of Jyväskylä, Finland); Richard Allmendinger (University of Manchester); and Vesa Ojalehto and Kaisa Miettinen (University of Jyväskylä)
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A set-oriented MOEA/D (Page 617)
Bilel Derbel and Arnaud Liefooghe (Univ. Lille, Inria Lille - Nord Europe); Qingfu Zhang (City University of Hong Kong); Sébastien Verel (Université du Littoral Côte d'Opale); and Hernan Aguirre and Kiyoshi Tanaka (Shinshu University)
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Collaborative Multi-Objective Optimization for Distributed Design of Complex Products (Page 625)
Joao Antonio Fialho Vilas Boas Duro, Yiming Yan, Robin C. Purshouse, and Peter J. Fleming (The University of Sheffield)
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Multi-Objective Evolutionary Hyper-heuristic based on Multiple Indicator-based Density Estimators (Page 633)
Jesús Guillermo Falcón-Cardona and Carlos Artemio Coello Coello (CINVESTAV-IPN)
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MOEA/D with Uniformly Randomly Adaptive Weights (Page 641)
Lucas Rodolfo Celestino Farias, Pedro Henrique Magalhães Braga, Hansenclever França Bassani, and Aluizio Fausto Ribeiro Araújo (Universidade Federal de Pernambuco)
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A Taxonomy of Methods for Visualizing Pareto Front Approximations (Page 649)
Bogdan Filipic and Tea Tusar (Jozef Stefan Institute)
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Data-Driven Analysis of Pareto Set Topology (Page 657)
Naoki Hamada and Keisuke Goto (Fujitsu Laboratories Ltd.)
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Dual-Grid Model of MOEA/D for Evolutionary Constrained Multiobjective Optimization (Page 665)
Hisao Ishibuchi (Southern University of Science and Technology) and Takafumi Fukase, Naoki Masuyama, and Yusuke Nojima (Osaka Prefecture University)
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Less Detectable Environmental Changes in Dynamic Multiobjective Optimisation (Page 673)
Shouyong Jiang and Marcus Kaiser (Newcastle University), Jinglei Guo (Central China Normal University), Shengxiang Yang (De Montfort University), and Natalio Krasnogor (Newcastle University)
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Multiobjective Sparse Unmixing Approach with Noise Removal (Page 681)
Xiangming Jiang, Maoguo Gong, Tao Zhan, and Zedong Tang (The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University)
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Component-level study of a decomposition-based multi-objective optimizer on a limited evaluation budget (Page 689)
Oliver P. H. Jones, Jeremy E. Oakley, and Robin C. Purshouse (The University of Sheffield)
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Efficient Search Techniques Using Adaptive Discretization of Design Variables on Real-Coded Evolutionary Computations (Page 697)
Toshiki Kondo and Tomoaki Tatsukawa (Tokyo University of Science)
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Improving the Performance of MO-RV-GOMEA on Problems with Many Objectives using Tchebycheff Scalarizations (Page 705)
Ngoc Hoang Luong (Centrum Wiskunde & Informatica (CWI)), Tanja Alderliesten (Academic Medical Center), and Peter A.N. Bosman (Centrum Wiskunde & Informatica (CWI))
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An Improved Version of a Reference-Based Multi-Objective Evolutionary Algorithm based on IGD+ (Page 713)
Edgar Manoatl Lopez and Carlos Artemio Coello Coello (CINVESTAV-IPN)
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Directed Mating in Decomposition-based MOEA for Constrained Many-objective Optimization (Page 721)
Minami Miyakawa (Hosei University, JSPS Research Fellow); Hiroyuki Sato (The University of Electro-Communications); and Yuji Sato (Hosei University)
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Transfer Strategies from Single- to Multi-objective Grouping Mechanisms (Page 729)
Frederick Sander, Heiner Zille, and Sanaz Mostaghim (Otto-von-Guericke University Magdeburg)
Supplemental Material: pap239s3-file2.pdf


mQAPViz: A divide-and-conquer multi-objective optimization algorithm to compute large data visualizations (Page 737)
Claudio Sanhueza, Francia Jiménez, Regina Berretta, and Pablo Moscato (The University of Newcastle)
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A new R2 indicator for better hypervolume approximation (Page 745)
Ke Shang (Southern University of Science and Technology, Southeast University); Hisao Ishibuchi (Southern University of Science and Technology, Osaka Prefecture Univeristy); Min-Ling Zhang (Southeast University); and Yiping Liu (Osaka Prefecture Univeristy)
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Parallel Pareto Local Search Revisited - First experimental results on Bi-objective UBQP (Page 753)
Jialong Shi (Xi'an Jiaotong University); Qingfu Zhang (City University of Hong Kong); Bilel Derbel and Arnaud Liefooghe (Univ. Lille, Inria Lille - Nord Europe); and Jianyong Sun (Xi'an Jiaotong University)
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Interactive Multiobjective Optimisation: Preference Changes And Algorithm Responsiveness (Page 761)
Kendall Peter Taylor and Xiaodong Li (RMIT University)
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Preference-based 3-Dimensional En-route Airspace Sectorization (Page 769)
Cheryl Sze Yin Wong and Suresh Sundaram (Nanyang Technological University)
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Evolutionary Computation plus Dynamic Programming for the Bi-Objective Travelling Thief Problem (Page 777)
Junhua Wu (The University of Adelaide), Sergey Polyakovskiy (Deakin University), and Markus Wagner and Frank Neumann (The University of Adelaide)
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A Steady-State NSGA-II based Multi-objective Multicast Routing Algorithm for Optical Networks (Page 785)
YING XU and YAN ZHOU (Hunan University)
Supplemental Material: pap334s3-file2.pdf


Two Enhancements for Improving the Convergence Speed of a Robust Multi-Objective Coevolutionary Algorithm (Page 793)
Alexandru-Ciprian Zavoianu, Susanne Saminger-Platz, and Edwin Lughofer (Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz) and Wolfgang Amrhein (Institute for Electrical Drives and Power Electronics, Johannes Kepler University Linz)
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Track: Evolutionary Numerical Optimization (back to top)

Drift Theory in Continuous Search Spaces: Expected Hitting Time of the (1+1)-ES with 1/5 Success Rule (Page 801)
Youhei Akimoto (University of Tsukuba), Anne Auger (Inria and Ecole Polytechnique), and Tobias Glasmachers (Ruhr-University Bochum)
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Analysis of Evolution Strategies with the Optimal Weighted Recombination (Page 809)
Chun-kit Au (Toronto, Ontario) and Ho-fung Leung (The Chinese University of Hong Kong)
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An Empirical Comparison of Metamodeling Strategies in Noisy Environments (Page 817)
Sunith Bandaru and Amos H.C. Ng (University of Skövde)
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Learning-Based Topology Variation in Evolutionary Level Set Topology Optimization (Page 825)
Mariusz Bujny (Technical University of Munich), Nikola Aulig and Markus Olhofer (Honda Research Institute Europe GmbH), and Fabian Duddeck (Technical University of Munich)
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A Global Information Based Adaptive Threshold for Grouping Large Scale Global Optimization Problems (Page 833)
An Chen and Yipeng Zhang (Xi'an Jiaotong University); Yang Yang (Xi’an Jiaotong University Shenzhen Research School); and Zhigang Ren, Yongsheng Liang, and Bei Pang (Xi'an Jiaotong University)
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Inheritance-Based Diversity Measures for Explicit Convergence Control in Evolutionary Algorithms (Page 841)
Thomas Gabor, Lenz Belzner, and Claudia Linnhoff-Popien (LMU Munich)
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Expanding variational autoencoders for learning and exploiting latent representations in search distributions (Page 849)
Unai Garciarena, Roberto Santana, and Alexander Mendiburu (University of the Basque Country)
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Real-Valued Evolutionary Multi-Modal Optimization driven by Hill-Valley Clustering (Page 857)
Stef C. Maree and Tanja Alderliesten (Academic Medical Center), Dirk Thierens (Utrecht University), and Peter A.N. Bosman (Centrum Wiskunde & Informatica (CWI))
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PSA-CMA-ES: CMA-ES with Population Size Adaptation (Page 865)
Kouhei Nishida (Shinshu University) and Youhei Akimoto (University of Tsukuba)
Supplemental Material: pap140s3-file2.pdf


Performance Improvements for Evolutionary Strategy-based One-Class Constraint Synthesis (Page 873)
Tomasz P. Pawlak (Institute of Computing Science, Poznan University of Technology)
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A Novel Similarity-based Mutant Vector Generation Strategy for Differential Evolution (Page 881)
Eduardo Segredo (Edinburgh Napier University, Universidad de La Laguna); Eduardo Lalla-Ruiz (University of Hamburg); and Emma Hart (Edinburgh Napier University)
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Adaptive Threshold Parameter Estimation with Recursive Differential Grouping for Problem Decomposition (Page 889)
Yuan Sun (The University of Melbourne), Mohammad Nabi Omidvar (The University of Birmingham), Michael Kirley (The University of Melbourne), and Xiaodong Li (RMIT University)
Supplemental Material: pap197s3-file2.zip


Analysis of Information Geometric Optimization with Isotropic Gaussian Distribution Under Finite Samples (Page 897)
Kento Uchida (Yokohama National University), Youhei Akimoto (University of Tsukuba), and Shinichi Shirakawa (Yokohama National University)
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Track: Genetic Algorithms (back to top)

Analysis of the use of genetic algorithms for indoor localisation via cloud point matching (Page 905)
Miguel d'Arcangues Boland and Leandro Soares Indrusiak (University of York)
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Tunneling Between Plateaus: Improving on a State-of-the-Art MAXSAT Solver using Partition Crossover (Page 921)
Wenxiang Chen (Colorado State University); Darrell Whitley (Colorado State University, Computer Science); Francisco Chicano (University of Málaga); and Renato Tinos (University of Saõ Paulo)
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Fast Algorithm for Fair Comparison of Genetic Algorithms (Page 913)
Chia-Sheng Chen, Hung-Wei Hsu, and Tian-Li Yu (National Taiwan University)
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Runtime Analysis of Probabilistic Crowding and Restricted Tournament Selection for Bimodal Optimisation (Page 929)
Edgar Covantes Osuna and Dirk Sudholt (The University of Sheffield)
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Shaper-GA: Automatic Shape Generation for Modular House Design (Page 937)
Ana Maria Carvalho de Almeida (ISCTE-IUL, CISUC & ISTAR-IUL); Bruno Taborda (ISCTE-IUL, CISUC); Filipe Santos (ISCTE-IUL, ISTAR-IUL); Krystian Kwiecinski (Warsaw University of Technology); and Sara Eloy (ISCTE-IUL, ISTAR-IUL)
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Simple On-the-Fly Parameter Selection Mechanisms for Two Classical Discrete Black-Box Optimization Benchmark Problems (Page 943)
Carola Doerr (Sorbonne University, CNRS) and Markus Wagner (The University of Adelaide)
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Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization: Profiling $(1+\lambda)$ EA Variants on OneMax and LeadingOnes (Page 951)
Carola Doerr (CNRS and Sorbonne University) and Furong Ye, Sander van Rijn, Hao Wang, and Thomas Bäck (Leiden University)
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Serendipitous Scaffolding to improve a Genetic Algorithm's Speed and Quality (Page 959)
Heather J. Goldsby (Michigan State University), Rebecca L. Young (University of Texas at Austin), Jory Schossau (Michigan State University), Hans A. Hofmann (University of Texas at Austin), and Arend Hintze (Michigan State University)
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On the Runtime Dynamics of the Compact Genetic Algorithm on Jump Functions (Page 967)
Václav Hasenöhrl and Andrew M. Sutton (University of Minnesota Duluth)
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Investigation of the Exponential Population Scheme for Genetic Algorithms (Page 975)
Yuen-Jen Lin and Tian-Li Yu (National Taiwan University)
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Learning an Evolvable Genotype-Phenotype Mapping (Page 983)
Matthew Andres Moreno, Banzhaf Wolfgang, and Charles Ofria (Michigan State University)
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Discrepancy-Based Evolutionary Diversity Optimization (Page 991)
Aneta Neumann and Wanru Gao (The University of Adelaide), Carola Doerr (CNRS and Sorbonne Universite), and Frank Neumann and Markus Wagner (The University of Adelaide)
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Grammatical Evolution Algorithm for Evolution of Swarm Behaviors (Page 999)
Aadesh Neupane, Michael A. Goodrich, and Eric G. Mercer (Brigham Young University)
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Learning Bayesian Network Structures with GOMEA (Page 1007)
Kalia Orphanou (Centrum Wiskunde & Informatica (CWI)), Dirk Thierens (Utrecht University), and Peter A.N. Bosman (Centrum Wiskunde & Informatica (CWI))
Supplemental Material:



Track: General Evolutionary Computation and Hybrids (back to top)

On the Runtime Analysis of Selection Hyper-Heuristics with Adaptive Learning Periods (Page 1015)
Benjamin Doerr (Ecole Polytechnique, Laboratoire d'Informatique (LIX)) and Andrei Lissovoi, Pietro S. Oliveto, and John Alasdair Warwicker (University of Sheffield)
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Sequential Sampling for Noisy Optimisation with CMA-ES (Page 1023)
Matthew Groves and Juergen Branke (University of Warwick)
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Adaptive Asynchrony in Semi-Asynchronous Evolutionary Algorithm Based on Performance Prediction Using Search History (Page 1031)
Tomohiro Harada (Ritsumeikan University)
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Expected Improvement of Constraint Violation for Expensive Constrained Optimization (Page 1039)
Ruwang Jiao and Sanyou Zeng (China University of Geosciences); Changhe Li (China University of Geosciences, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems); and Yuhong Jiang and Junchen Wang (China University of Geosciences)
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Talakat: Bullet Hell Generation through Constrained Map-Elites (Page 1047)
Ahmed Khalifa, Scott Lee, Andy Nealen, and Julian Togelius (New York University)
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Neural Estimation of Interaction Outcomes (Page 1055)
Paweł Liskowski, Bartosz Wieloch, and Krzysztof Krawiec (Poznan University of Technology/Laboratory of Intelligent Decision Support Systems)
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Termination Detection Strategies in Evolutionary Algorithms: A Survey (Page 1063)
Yanfeng Liu and Aimin Zhou (East China Normal University) and Hu Zhang (Beijing Electro-mechanical Engineering Institute)
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Memetic Algorithms Beat Evolutionary Algorithms on the Class of Hurdle Problems (Page 1071)
Phan Trung Hai Nguyen (University of Birmingham) and Dirk Sudholt (University of Sheffield)
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Cooperative Co-evolution with Online Optimizer Selection for Large-Scale Optimization (Page 1079)
Yuan Sun and Michael Kirley (The University of Melbourne) and Xiaodong Li (RMIT University)
Supplemental Material: pap635s3-file2.zip


Quasi-Bistability of Walk-Based Landscape Measures in Stochastic Fitness Landscapes (Page 1087)
Bernhard Werth (Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria; Institute for Formal Models and Verification,Johannes Keppler University Linz); Erik Pitzer (Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria); Gerald Ostermayer (Research Group Networks and Mobility, University of Applied Sciences Upper Austria); and Michael Affenzeller (Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria; Institute for Formal Models and Verification,Johannes Keppler University Linz)
Supplemental Material: pap151s3-file2.pdf


Changing or Keeping Solutions in Dynamic Optimization Problems with Switching Costs (Page 1095)
Danial Yazdani (LJMU), Juergen Branke (Warwick Business School), Mohammad Nabi Omidvar (CERCIA), Trung Thanh Nguyen (LJMU), and Xin Yao (CERCIA)
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Working Principles of Binary Differential Evolution (Page 1103)
Weijie Zheng and Guangwen Yang (Department of Computer Science and Technology, Tsinghua University) and Benjamin Doerr (Ecole Polytechnique, Laboratoire d'Informatique (LIX))
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Track: Genetic Programming (back to top)

Schema-based Diversification in Genetic Programming (Page 1111)
Bogdan Burlacu and Michael Affenzeller (University of Applied Sciences Upper Austria)
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Towards Effective Semantic Operators for Program Synthesis in Genetic Programming (Page 1119)
Stefan Forstenlechner, David Fagan, Miguel Nicolau, and Michael O'Neill (University College Dublin)
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Program Synthesis using Uniform Mutation by Addition and Deletion (Page 1127)
Thomas Helmuth (Hamilton College); Nicholas Freitag McPhee (University of Minnesota, Morris); and Lee Spector (Hampshire College)
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Evolving Event-driven Programs with SignalGP (Page 1135)
Alexander Lalejini and Charles Ofria (Michigan State University, BEACON Center for the Study of Evolution in Action)
Supplemental Material: pap303s3-file2.zip


Neuro-Guided Genetic Programming: Prioritizing Evolutionary Search with Neural Networks (Page 1143)
Paweł Liskowski, Iwo Błądek, and Krzysztof Krawiec (Poznan University of Technology/Laboratory of Intelligent Decision Support Systems)
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Solving the Exponential Growth of Symbolic Regression Trees in Geometric Semantic Genetic Programming (Page 1151)
Joao Francisco Barreto da Silva Martins, Luiz Otavio Vilas Boas Oliveira, Luis Fernando Miranda, Felipe Casadei, and Gisele Lobo Pappa (Universidade Federal de Minas Gerais)
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Genetic Programming Approach to Learning Multi-pass Heuristics for Resource Constrained Job Scheduling (Page 1167)
SU Nguyen (La Trobe University), Dhananjay Thiruvady and Andreas Ernst (Monash University), and Damminda Alahakoon (La Trobe University)
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Adaptive Charting Genetic Programming for Dynamic Flexible Job Shop Scheduling (Page 1159)
Su Nguyen (La Trobe University), Mengjie Zhang (Victoria University of Wellington), Kay Chen Tan (City University of Hong Kong), and Damminda Alahakoon (La Trobe University)
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Measuring Evolvability and Accessibility using the Hyperlink-Induced Topic Search Algorithm (Page 1175)
Kyle Nickerson and Yuanzhu Chen (Memorial University), Feng Wang (Wuhan University), and Ting Hu (Memorial University)
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Where are we now? A large benchmark study of recent symbolic regression methods (Page 1183)
Patryk Orzechowski, William La Cava, and Jason H. Moore (University of Pennsylvania)
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An Analysis of the Bias of Variation Operators of Estimation of Distribution Programming (Page 1191)
Dirk Schweim and Franz Rothlauf (Johannes Gutenberg University Mainz)
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Track: Real World Applications (back to top)

Large-Scale Parallelization of Partial Evaluations in Evolutionary Algorithms for Real-World Problems (Page 1199)
Anton Bouter (Centrum Wiskunde & Informatica), Tanja Alderliesten and Arjan Bel (Academic Medical Center), Cees Witteveen (Delft University of Technology), and Peter A.N. Bosman (Centrum Wiskunde & Informatica)
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A Rolling Window with Genetic Algorithm Approach to Sorting Aircraft for Automated Taxi Routing (Page 1207)
Alexander Edward Ian Brownlee (University of Stirling) and John Robert Woodward, Michal Weiszer, and Jun Chen (Queen Mary University of London)
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Genetic Programming for Tuberculosis Screening from Raw X-ray Images (Page 1214)
Armand Rashad Burks and William Fitzgerald Punch (Michigan State University)
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Value-Based Manufacturing Optimisation in Serverless Clouds for Industry 4.0 (Page 1222)
Piotr Dziurzanski, Jerry Swan, and Leandro Soares Indrusiak (University of York)
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Rate-Setter : Roadmap for Faster, Safer, and Better Platform Train Interface Design and Operation using Evolutionary Optimization (Page 1230)
David Fletcher, Rob Harrison, Twin Karmakharm, Paul Richmond, and Samadhi Nallaperuma (The University of Sheffield)
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Surrogate assisted optimization of particle reinforced metal matrix composites (Page 1238)
Lorenzo Gentile and Martin Zaefferer (TH Köln - University of Applied Sciences), Dario Giugliano and Haofeng Chen (University of Strathclyde), and Thomas Bartz-Beielstein (TH Köln - University of Applied Sciences)
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Optimizing Residential Energy Resources with an Improved Multi-Objective Genetic Algorithm based on Greedy Mutations (Page 1246)
Ivo Gonçalves, Álvaro Gomes, and Carlos Henggeler Antunes (INESC Coimbra, DEEC, University of Coimbra)
Supplemental Material: pap590s3-file2.zip


Investigation of the Latent Space of Stock Market Patterns with Genetic Programming (Page 1254)
Sungjoo Ha, Sangyeop Lee, and Byung-Ro Moon (Seoul National University)
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Multi-Objective Journey Planning Under Uncertainty: A Genetic Approach (Page 1262)
Mohammad Haqqani, Xiaodong Li, and Xinghuo Yu (RMIT University)
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Genetic Algorithm to Study Practical Quantum Adversaries (Page 1270)
Walter O. Krawec and Sam A. Markelon (University of Connecticut)
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Predicting Friction System Performance with Symbolic Regression and Genetic Programming with Factor Variables (Page 1278)
Gabriel Kronberger (University of Applied Sciences Upper Austria; School of Informatics, Communications and Media); Michael Kommenda (University of Applied Sciences Upper Austria); and Andreas Promberger and Falk Nickel (Miba Frictec)
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Benchmarking Evolutionary Computation Approaches to Insider Threat Detection (Page 1286)
Duc C. Le, Sara Khanchi, A. Nur Zincir-Heywood, and Malcolm I. Heywood (Dalhousie University)
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Informed Mutation Operator using Machine Learning for Optimization in Epidemics Prevention (Page 1294)
Krzysztof Michalak (Wroclaw University of Economics)
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Evolving Boolean Functions for Fast and Efficient Randomness Testing (Page 1302)
Vojtech Mrazek (Brno University of Technology), Marek Sýs (Masaryk University Brno), Zdenek Vasicek and Lukas Sekanina (Brno University of Technology), and Vashek Matyas (Masaryk University Brno)
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Estimation of the Heterogeneous Strategies from Action Log (Page 1310)
Keiichi Namikoshi and Sachiyo Arai (Chiba University)
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A Detailed Comparison of Meta-Heuristic Methods for Optimising Wave Energy Converter Positions (Page 1318)
Mehdi Neshat, Bradley Alexander, Markus Wagner, and Yuanzhong Xia (Optimization and Logistics Group, School of Computer Science, The University of Adelaide)
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Evolving the Autosteering of a Car Featuring a Realistically Simulated Steering Response (Page 1326)
Vsevolod Nikulin, Albert Podusenko, Ivan Tanev, and Katsunori Shimohara (Doshisha University)
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Multi-Objective Aerodynamic Design with User Preference using Truncated Expected Hypervolume Improvement (Page 1333)
Pramudita Satria Palar (Tohoku University), Kaifeng Yang (Leiden University), Koji Shimoyama (Tohoku University), and Michael Emmerich and Thomas Bäck (Leiden University)
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Impacts of Constraints and Constraint Handling Strategies for Multi-Objective Mechanical Design Problems (Page 1341)
Cyril Picard and Jürg Schiffmann (École Polytechnique Fédérale de Lausanne)
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Comparison of Parallel Surrogate-Assisted Optimization Approaches (Page 1348)
Frederik Rehbach, Martin Zaefferer, Jörg Stork, and Thomas Bartz-Beielstein (TH Köln)
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Orthogonalization of Linear Representations for Efficient Evolutionary Design Optimization (Page 1356)
Andreas Richter and Stefan Dresselhaus (Bielefeld University), Stefan Menzel (Honda Research Institute Europe GmbH), and Mario Botsch (Bielefeld University)
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A Genetic Programming based Iterated Local Search for Software Project Scheduling (Page 1364)
Nasser R. Sabar (La Trobe University) and Ayad Turky and Andy Song (RMIT University)
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A GA based Network Optimization Tool for Passive In-Building Distributed Antenna Systems (Page 1371)
Siddhartha Shakya, Kin Poon, and Anis Ouali (EBTIC, Khalifa University)
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Functional Generative Design: An Evolutionary Approach to 3D-Printing (Page 1379)
Cem C. Tutum, Supawit Chockchowwat, and Etienne Vouga (The University of Texas at Austin) and Risto Miikkulainen (The University of Texas at Austin, Sentient Technologies Inc.)
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Better and Faster Catheter Position Optimization in HDR Brachytherapy for Prostate Cancer using Multi-Objective Real-Valued GOMEA (Page 1387)
Marjolein C. van der Meer and Bradley R. Pieters (Academic Medical Center), Yury Niatsetski (Elekta), Tanja Alderliesten and Arjan Bel (Academic Medical Center), and Peter A.N. Bosman (Centrum Wiskunde & Informatica)
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Symbolic Regression and Feature Construction with GP-GOMEA applied to Radiotherapy Dose Reconstruction of Childhood Cancer Survivors (Page 1395)
Marco Virgolin (Centrum Wiskunde & Informatica), Tanja Alderliesten and Arjan Bel (Academic Medical Center), Cees Witteveen (Delft University of Technology), and Peter A.N. Bosman (Centrum Wiskunde & Informatica (CWI))
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Estimating Cement Compressive Strength from Microstructural Images using GEP with Probabilistic Polarized Similarity Weight Tournament Selection (Page 1403)
Liangliang Zhang, Xinya Yue, Lin Wang, and Bo Yang (University of Jinan)
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Track: Search-Based Software Engineering (back to top)

Multi-Objective Black-Box Test Case Selection for Cost-Effectively Testing Simulation Models (Page 1411)
Aitor Arrieta (Mondragon University); Shuai Wang (Simula Research Laboratory); and Ainhoa Arruabarrena, Urtzi Markiegi, Goiuria Sagardui, and Leire Etxeberria (Mondragon University)
Supplemental Material: pap237s3-file2.zip


On the Effects of Seeding Strategies: A Case for Search-based Multi-Objective Service Composition (Page 1419)
Tao Chen (Department of Computing and Technology, Nottingham Trent University, UK; CERCIA, School of Computer Science, University of Birmingham, UK); Miqing Li (CERCIA, School of Computer Science, University of Birmingham, UK); and Xin Yao (Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China; CERCIA, School of Computer Science, University of Birmingham, UK)
Supplemental Material:


Test suite minimization for mutation testing of WS-BPEL compositions (Page 1427)
Francisco Palomo-Lozano, Inmaculada Medina-Bulo, and Antonia Estero-Botaro (Universidad de Cádiz) and Manuel Núñez (Universidad Complutense de Madrid)
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Towards the Automated Recovery of Complex Temporal API-Usage Patterns (Page 1435)
Mohamed Aymen Saied (Concordia University) and Houari Sahraoui, Edouard Batot, Michalis Famelis, and Pierre-Olivier Talbot (DIRO, Université de Montréal)
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A Novel Fitness Function for Automated Program Repair Based on Source Code Checkpoints (Page 1443)
Eduardo Faria de Souza and Celso Gonçalves Camilo-Junior (Universidade Federal de Goiás) and Claire Le Goues (Carnegie Mellon University)
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Dependent Input Sampling Strategies: Using Metaheuristics for Generating Parameterised Random Sampling Regimes (Page 1451)
Komsan Srivisut (University of York), John Andrew Clark (University of Sheffield), and Richard Freeman Paige (University of York)
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Track: Theory (back to top)

A Tight Runtime Analysis for the $(\mu+\lambda)$ EA (Page 1459)
Denis Antipov (ITMO University); Benjamin Doerr (Ecole Polytechnique, Laboratoire d'Informatique (LIX)); and Jiefeng Fang and Tangi Hetet (Ecole Polytechnique)
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A New Analysis Method for Evolutionary Optimization of Dynamic and Noisy Objective Functions (Page 1467)
Raphaël Dang-Nhu and Thibault Dardinier (École Polytechnique); Benjamin Doerr (Ecole Polytechnique, Laboratoire d'Informatique (LIX)); Gautier Izacard (École Polytechnique); and Dorian Nogneng (École Polytechnique, Laboratoire d'Informatique (LIX))
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Significance-based Estimation-of-Distribution Algorithms (Page 1483)
Benjamin Doerr (École Polytechnique, Laboratoire d'Informatique (LIX)) and Martin S. Krejca (Hasso Plattner Institute)
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Runtime Analysis for Self-adaptive Mutation Rates (Page 1475)
Benjamin Doerr (Ecole Polytechnique, Laboratoire d'Informatique (LIX)); Carsten Witt (Technical University of Denmark); and Jing Yang (Ecole Polytechnique, Laboratoire d'Informatique (LIX))
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The linear hidden subset problem for the (1+1) EA with scheduled and adaptive mutation rates (Page 1491)
Hafsteinn Einarsson, Marcelo Matheus Gauy, Johannes Lengler, Florian Meier, Asier Mujika, Angelika Steger, and Felix Weissenberger (ETH Zurich)
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Medium Step Sizes are Harmful for the Compact Genetic Algorithm (Page 1499)
Johannes Lengler (ETH Zürich), Dirk Sudholt (The University of Sheffield), and Carsten Witt (Technical University Of Denmark)
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Analysis of Noisy Evolutionary Optimization When Sampling Fails (Page 1507)
Chao Qian and Chao Bian (University of Science and Technology of China), Yang Yu (Nanjing University), and Ke Tang and Xin Yao (Southern University of Science and Technology)
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Runtime Analysis of Randomized Search Heuristics for the Dynamic Weighted Vertex Cover Problem (Page 1515)
Feng Shi (Central South University), Frank Neumann (The University of Adelaide), and Jianxin Wang (Central South University)
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On the Robustness of Evolutionary Algorithms to Noise: Refined Results and an Example Where Noise Helps (Page 1523)
Dirk Sudholt (University of Sheffield)
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Crossover Can Simulate Bounded Tree Search on a Fixed-Parameter Tractable Optimization Problem (Page 1531)
Andrew M. Sutton (University of Minnesota Duluth)
Supplemental Material: pap524s3-file2.pdf


Domino Convergence: Why One Should Hill-Climb on Linear Functions (Page 1539)
Carsten Witt (Technical University Of Denmark)
Supplemental Material: