Table of Contents

• Evolutionary Computation EvolvingKenneth De Jong. 1 [doi]
• AI for Scientific Discovery and a Sustainable FutureCarla Pedro Gomes. 2 [doi]
• Super-Human and Super-AI Cognitive Augmentation of Human and Human-AI Teams Assisted by Brain Computer InterfacesRiccardo Poli. 3 [doi]
• On the evolution of mechanisms for three-option collective decision-making in a swarm of simulated robotsAhmed Almansoori, Muhanad AlKilabi, Elio Tuci. 4-12 [doi]
• The Barrier Tree Benchmark: Many Basins and Double FunnelsTim Blackwell. 13-20 [doi]
• Aggregation Through Adaptive Random Walks in a Minimalist Robot SwarmLuigi Feola, Antoine Sion, Vito Trianni, Andreagiovanni Reina, Elio Tuci. 21-29 [doi]
• Factored Particle Swarm Optimization for Policy Co-training in Reinforcement LearningKordel K. France, John W. Sheppard. 30-38 [doi]
• Pid-Inspired Modifications in Response Threshold Models In Swarm Intelligent SystemsMaryam Kebari, Annie S. Wu, H. David Mathias. 39-46 [doi]
• Learning-Based Neural Ant Colony OptimizationYi Liu, Jiang Qiu, Emma Hart, Yilan Yu, Zhongxue Gan, Wei Li 0055. 47-55 [doi]
• Leveraging Human Feedback to Evolve and Discover Novel Emergent Behaviors in Robot SwarmsConnor Mattson, Daniel S. Brown. 56-64 [doi]
• The Impact of Morphological Diversity in Robot SwarmsGeoff Nitschke, Sindiso Mkhatshwa. 65-74 [doi]
• Swarms of Artificial Platelets for Emergent Hole Detection and Healing in Wireless Sensor NetworksGiada Simionato, Federico A. Galatolo, Mario G. C. A. Cimino. 75-83 [doi]
• A Study of Ant-Based Pheromone Spaces for Generation Perturbative Hyper-HeuristicsEmilio Singh, Nelishia Pillay. 84-92 [doi]
• Particle Swarm Optimization with Ring Topology for Multi-modal Multi-objective ProblemsYouwei Sun, Chaoli Sun. 93-101 [doi]
• Variation Encoded Large-Scale Swarm Optimizers for Path Planning of Unmanned Aerial VehicleTan-Lin Xiao, Qiang Yang, Xu-Dong Gao, Yuan-yuan Ma, Zhen Yu Lu, Sang-Woon Jeon, Jun Zhang 0003. 102-110 [doi]
• Region-based Evaluation Particle Swarm Optimization with Dual Solution Libraries for Real-time Traffic Signal Timing OptimizationChi Zhang, Jian-Yu Li, Chun-Hua Chen, Yun Li 0002, Zhi-hui Zhan. 111-118 [doi]
• Gradient-Informed Quality Diversity for the Illumination of Discrete SpacesRaphaël Boige, Guillaume Richard, Jérémie Donà, Thomas Pierrot, Antoine Cully. 119-128 [doi]
• Differentiable Soft-Robot GenerationFrançois Cochevelou, David Bonner, Martin-Pierre Schmidt. 129-137 [doi]
• MAP-Elites with Descriptor-Conditioned Gradients and Archive Distillation into a Single PolicyMaxence Faldor, Félix Chalumeau, Manon Flageat, Antoine Cully. 138-146 [doi]
• Selection for short-term empowerment accelerates the evolution of homeostatic neural cellular automataCaitlin Grasso, Josh C. Bongard. 147-155 [doi]
• Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain DomainsLuca Grillotti, Manon Flageat, Bryan Lim, Antoine Cully. 156-164 [doi]
• Improving the Data Efficiency of Multi-Objective Quality-Diversity through Gradient Assistance and Crowding ExplorationHannah Janmohamed, Thomas Pierrot, Antoine Cully. 165-173 [doi]
• Modular Controllers Facilitate the Co-Optimization of Morphology and Control in Soft RobotsAlican Mertan, Nick Cheney. 174-183 [doi]
• A Fully-distributed Shape-aware Neural Controller for Modular RobotsGiorgia Nadizar, Eric Medvet, Kathryn Walker, Sebastian Risi. 184-192 [doi]
• Universal Mechanical Polycomputation in Granular MatterAtoosa Parsa, Sven Witthaus, Nidhi Pashine, Corey S. O'Hern, Rebecca Kramer-Bottiglio, Josh C. Bongard. 193-201 [doi]
• Optimization of a Hydrodynamic Computational Reservoir through EvolutionAlessandro Pierro, Kristine Heiney, Shamit Shrivastava, Giulia Marcucci, Stefano Nichele. 202-210 [doi]
• Morphology Choice Affects the Evolution of Affordance Detection in RobotsFederico Pigozzi, Stephanie J. Woodman, Eric Medvet, Rebecca Kramer-Bottiglio, Josh C. Bongard. 211-219 [doi]
• pyribs: A Bare-Bones Python Library for Quality Diversity OptimizationBryon Tjanaka, Matthew C. Fontaine, David H. Lee, Yulun Zhang, Nivedit Reddy Balam, Nathaniel Dennler, Sujay S. Garlanka, Nikitas Dimitri Klapsis, Stefanos Nikolaidis. 220-229 [doi]
• Estimation-of-Distribution Algorithms for Multi-Valued Decision VariablesFiras Ben Jedidia, Benjamin Doerr, Martin S. Krejca. 230-238 [doi]
• New Knowledge about the Elementary Landscape Decomposition for Solving the Quadratic Assignment ProblemXabier Benavides, Josu Ceberio, Leticia Hernando, José Antonio Lozano 0001. 239-247 [doi]
• On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson ProblemJakob Bossek, Aneta Neumann, Frank Neumann 0001. 248-256 [doi]
• To Combine or not to Combine Graybox Crossover and Local Search?Lorenzo Canonne, Bilel Derbel, Francisco Chicano, Gabriela Ochoa. 257-265 [doi]
• Learning to Select Initialisation Heuristic for Vehicle Routing ProblemsJoão Guilherme Cavalcanti Costa, Yi Mei 0001, Mengjie Zhang 0001. 266-274 [doi]
• Fourier Transform-based Surrogates for Permutation ProblemsFrancisco Chicano, Bilel Derbel, Sébastien Vérel. 275-283 [doi]
• Local Optima Markov Chain: A New Tool for Landscape-aware Analysis of Algorithm DynamicsFrancisco Chicano, Gabriela Ochoa, Bilel Derbel, Lorenzo Canonne. 284-292 [doi]
• Finding Near-Optimal Weight Independent Sets at ScaleErnestine Großmann, Sebastian Lamm, Christian Schulz 0003, Darren Strash. 293-302 [doi]
• MOEAs Are Stuck in a Different Area at a TimeMiqing Li, Xiaofeng Han, Xiaochen Chu. 303-311 [doi]
• Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-spaceAlejandro Marrero, Eduardo Segredo, Emma Hart, Jakob Bossek, Aneta Neumann. 312-320 [doi]
• Leveraging problem-independent hyper-heuristics for real-world test laboratory schedulingFlorian Mischek, Nysret Musliu. 321-329 [doi]
• A MILP-Based Very Large-Scale Neighborhood Search for the Heterogeneous Vehicle Routing Problem with Simultaneous Pickup and DeliveryNapoleão Nepomuceno, Ricardo Barboza Saboia, André L. V. Coelho. 330-338 [doi]
• Local Optima Correlation Assisted Adaptive Operator SelectionJiyuan Pei, Hao Tong, Jialin Liu 0001, Yi Mei 0001, Xin Yao 0001. 339-347 [doi]
• Pareto Local Search is Competitive with Evolutionary Algorithms for Multi-Objective Neural Architecture SearchQuan Minh Phan, Ngoc Hoang Luong. 348-356 [doi]
• Q-Learning Ant Colony Optimization supported by Deep Learning for Target Set SelectionJairo Enrique Ramírez Sánchez, Camilo Chacón Sartori, Christian Blum 0001. 357-366 [doi]
• Doubly Stochastic Matrix Models for Estimation of Distribution AlgorithmsValentino Santucci, Josu Ceberio. 367-374 [doi]
• On the Use of Second Order Neighbors to Escape from Local OptimaManuel Torralbo, Leticia Hernando, Ernesto Contreras-Torres, José Antonio Lozano 0001. 375-383 [doi]
• Sample-Aware Surrogate-Assisted Genetic Programming for Scheduling Heuristics Learning in Dynamic Flexible Job Shop SchedulingLuyao Zhu, Fangfang Zhang 0003, Xiaodong Zhu, Ke Chen, Mengjie Zhang 0001. 384-392 [doi]
• Producing Diverse Rashomon Sets of Counterfactual Explanations with Niching Particle Swarm Optimization AlgorithmsHayden Andersen, Andrew Lensen, Will N. Browne, Yi Mei 0001. 393-401 [doi]
• Novelty Seeking Multiagent Evolutionary Reinforcement LearningAyhan Alp Aydeniz, Robert Loftin, Kagan Tumer. 402-410 [doi]
• Symbolic Regression Trees as Embedded RepresentationsVictor Caetano, Matheus Cândido Teixeira, Gisele Lobo Pappa. 411-419 [doi]
• Relieving Genetic Programming from Coefficient Learning for Symbolic Regression via Correlation and Linear ScalingQi Chen, Bing Xue 0001, Wolfgang Banzhaf, Mengjie Zhang 0001. 420-428 [doi]
• Leveraging Fitness Critics To Learn Robust TeamworkJoshua Cook, Kagan Tumer, Tristan Scheiner. 429-437 [doi]
• Co-operative Co-evolutionary Many-objective Embedded Multi-label Feature Selection with Decomposition-based PSOKaan Demir, Bach Hoai Nguyen, Bing Xue 0001, Mengjie Zhang 0001. 438-446 [doi]
• Learning Synergies for Multi-Objective Optimization in Asymmetric Multiagent SystemsGaurav Dixit, Kagan Tumer. 447-455 [doi]
• Covariance Matrix Adaptation MAP-AnnealingMatthew Fontaine, Stefanos Nikolaidis. 456-465 [doi]
• Adaptive Team Cooperative Co-Evolution for a Multi-Rover Distribution ProblemNicolas Fontbonne, Nicolas Maudet, Nicolas Bredèche. 466-475 [doi]
• OmnImage: Evolving 1k Image Cliques for Few-Shot LearningLapo Frati, Neil Traft, Nick Cheney. 476-484 [doi]
• MOAZ: A Multi-Objective AutoML-Zero FrameworkRitam Guha, Wei Ao, Stephen Kelly, Vishnu Boddeti, Erik D. Goodman, Wolfgang Banzhaf, Kalyanmoy Deb. 485-492 [doi]
• Positive Definite Nonparametric Regression using an Evolutionary Algorithm with Application to Covariance Function EstimationMyeongjong Kang. 493-501 [doi]
• Hybridizing TPOT with Bayesian OptimizationAngus Kenny, Tapabrata Ray, Steffen Limmer, Hemant Kumar Singh, Tobias Rodemann, Markus Olhofer. 502-510 [doi]
• Optimizing fairness tradeoffs in machine learning with multiobjective meta-modelsWilliam G. La Cava. 511-519 [doi]
• Dynamic Depth for Better Generalization in Continued Fraction RegressionPablo Moscato, Andrew Ciezak, Nasimul Noman. 520-528 [doi]
• Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem InstancesAna Nikolikj, Saso Dzeroski, Mario Andrés Muñoz, Carola Doerr, Peter Korosec, Tome Eftimov. 529-537 [doi]
• Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning ModelsLennart Schneider, Bernd Bischl, Janek Thomas. 538-547 [doi]
• Fuzzy-UCS Revisited: Self-Adaptation of Rule Representations in Michigan-Style Learning Fuzzy-Classifier SystemsHiroki Shiraishi, Yohei Hayamizu, Tomonori Hashiyama. 548-557 [doi]
• Biological insights on grammar-structured mutations improve fitness and diversityStefano Tiso, Pedro Carvalho, Nuno Lourenço 0002, Penousal Machado. 558-567 [doi]
• Semi-Supervised Learning with Coevolutionary Generative Adversarial NetworksJamal Toutouh, Subhash Nalluru, Erik Hemberg, Una-May O'Reilly. 568-576 [doi]
• A Two-Stage Multi-Objective Evolutionary Reinforcement Learning Framework for Continuous Robot ControlHai-Long Tran, Long Doan, Ngoc Hoang Luong, Huynh Thi Thanh Binh. 577-585 [doi]
• Interactive Latent Diffusion ModelMathurin Videau, Nickolai Knizev, Alessandro Leite, Marc Schoenauer, Olivier Teytaud. 586-596 [doi]
• Cooperative Co-Evolution for Ensembles of Nested Dichotomies for Multi-Class ClassificationMarcel Wever, Miran Özdogan, Eyke Hüllermeier. 597-605 [doi]
• Exploring High-dimensional Rules Indirectly via Latent Space Through a Dimensionality Reduction for XCSNaoya Yatsu, Hiroki Shiraishi, Hiroyuki Sato, Keiki Takadama. 606-614 [doi]
• An Effective One-Shot Neural Architecture Search Method with Supernet Fine-Tuning for Image ClassificationGonglin Yuan, Bing Xue 0001, Mengjie Zhang 0001. 615-623 [doi]
• Rethinking Population-assisted Off-policy Reinforcement LearningBowen Zheng, Ran Cheng. 624-632 [doi]
• Evolutionary Multi-Objective Deep Reinforcement Learning for Autonomous UAV Navigation in Large-Scale Complex EnvironmentsGuangyan An, Ziyu Wu, Zhilong Shen, Ke Shang, Hisao Ishibuchi. 633-641 [doi]
• Analysing the Robustness of NSGA-II under NoiseDuc-Cuong Dang, Andre Opris, Bahare Salehi, Dirk Sudholt. 642-651 [doi]
• Multiobjective Optimization with a Quadratic Surrogate-assisted CMA-ESMohamed Gharafi, Nikolaus Hansen, Dimo Brockhoff, Rodolphe Le Riche. 652-660 [doi]
• Effects of Including Optimal Solutions into Initial Population on Evolutionary Multiobjective OptimizationCheng Gong, Yang Nan 0001, Lie Meng Pang, Qingfu Zhang 0001, Hisao Ishibuchi. 661-669 [doi]
• Effects of Objective Space Normalization in Multi-Objective Evolutionary Algorithms on Real-World ProblemsLinjun He, Yang Nan 0001, Hisao Ishibuchi, Dipti Srinivasan. 670-678 [doi]
• Effects of Dominance Modification on Hypervolume-based and IGD-based Performance Evaluation Results of NSGA-IIHisao Ishibuchi, Lie Meng Pang, Ke Shang. 679-687 [doi]
• Improving Neighborhood Exploration Mechanism to Speed up PLSYuhao Kang, Jialong Shi, Jianyong Sun, Ye Fan. 688-694 [doi]
• Adaptive Donor Selection Mixing for Multi-objective Optimization: an Enhanced Variant of MO-GOMEAHsu Chen Liao, Wen Zhong Fang, Tian-Li Yu. 695-703 [doi]
• Many-objective (Combinatorial) Optimization is EasyArnaud Liefooghe, Manuel López-Ibáñez. 704-712 [doi]
• Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and ExpressivenessArnaud Liefooghe, Gabriela Ochoa, Sébastien Vérel, Bilel Derbel. 713-721 [doi]
• RM-SAEA: Regularity Model Based Surrogate-Assisted Evolutionary Algorithms for Expensive Multi-Objective OptimizationYongfan Lu, Bingdong Li, Hong Qian, Wenjing Hong, Peng Yang, Aimin Zhou. 722-730 [doi]
• 3-Objective Pareto Optimization for Problems with Chance ConstraintsFrank Neumann 0001, Carsten Witt. 731-739 [doi]
• Two-Phase Procedure for Efficiently Removing Dominated Solutions From Large Solution SetsTianye Shu, Yang Nan 0001, Ke Shang, Hisao Ishibuchi. 740-748 [doi]
• On the Unbounded External Archive and Population Size in Preference-based Evolutionary Multi-objective Optimization Using a Reference PointRyoji Tanabe. 749-758 [doi]
• Co-evolution improves the efficiency of preference learning methods when the Decision Maker's aspirations develop over timeMichal Tomczyk, Milosz Kadzinski. 759-767 [doi]
• Decomposition-Based Multi-Objective Evolutionary Algorithm with Model-Based Ideal Point EstimationYin Wu, Ruihao Zheng, Zhenkun Wang. 768-776 [doi]
• Directed Quick Search Guided Evolutionary Algorithm for Large-scale Multi-objective Optimization ProblemsYing Wu, Na Yang, Long Chen, Ye Tian, Zhenzhou Tang. 777-785 [doi]
• Multi-objective Robust Optimization and Decision-Making Using Evolutionary AlgorithmsDeepanshu Yadav, Palaniappan Ramu, Kalyanmoy Deb. 786-794 [doi]
• A hierarchical clustering-based cooperative multi-population many-objective optimization algorithmNa Yang, Quan Zhang, Ying Wu, Yisu Ge, Zhenzhou Tang. 795-803 [doi]
• STHV-Net: Hypervolume Approximation based on Set TransformerHan Zhu, Ke Shang, Hisao Ishibuchi. 804-812 [doi]
• DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization ProblemsGjorgjina Cenikj, Gasper Petelin, Carola Doerr, Peter Korosec, Tome Eftimov. 813-821 [doi]
• Evolutionary Mixed-Integer Optimization with Explicit ConstraintsYuan Hong, Dirk Arnold. 822-830 [doi]
• Natural Evolution Strategy for Mixed-Integer Black-Box OptimizationKoki Ikeda, Isao Ono. 831-838 [doi]
• CMA-ES with Learning Rate Adaptation: Can CMA-ES with Default Population Size Solve Multimodal and Noisy Problems?Masahiro Nomura, Youhei Akimoto, Isao Ono. 839-847 [doi]
• On a Population Sizing Model for Evolution Strategies Optimizing the Highly Multimodal Rastrigin FunctionLisa Schönenberger, Hans-Georg Beyer. 848-855 [doi]
• When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous ProblemsAndré Thomaser, Jacob de Nobel, Diederick Vermetten, Furong Ye, Thomas Bäck, Anna V. Kononova. 856-863 [doi]
• Modular Differential EvolutionDiederick Vermetten, Fabio Caraffini, Anna V. Kononova, Thomas Bäck. 864-872 [doi]
• Using Affine Combinations of BBOB Problems for Performance AssessmentDiederick Vermetten, Furong Ye, Carola Doerr. 873-881 [doi]
• (1+1)-CMA-ES with Margin for Discrete and Mixed-Integer ProblemsYohei Watanabe 0001, Kento Uchida, Ryoki Hamano, Shota Saito, Masahiro Nomura, Shinichi Shirakawa. 882-890 [doi]
• Probabilistic model with evolutionary optimization for cognitive diagnosisChenyang Bu, Zhiyong Cao, Chenlong He, Yuhong Zhang 0002. 891-899 [doi]
• Combining Evolutionary Algorithms with Reaction Rules Towards Focused Molecular DesignJoão Correia, Vitor Pereira 0001, Miguel Rocha 0001. 900-909 [doi]
• The Impact of Asynchrony on Parallel Model-Based EAsArthur Guijt, Dirk Thierens, Tanja Alderliesten, Peter A. N. Bosman. 910-918 [doi]
• Larger Offspring Populations Help the (1 + (λ, λlambda)) Genetic Algorithm to Overcome the NoiseAlexandra Ivanova, Denis Antipov, Benjamin Doerr. 919-928 [doi]
• Discovering Attention-Based Genetic Algorithms via Meta-Black-Box OptimizationRobert Tjarko Lange, Tom Schaul, Yutian Chen, Chris Lu 0001, Tom Zahavy, Valentin Dalibard, Sebastian Flennerhag. 929-937 [doi]
• Evolutionary Diversity Optimisation in Constructing Satisfying AssignmentsAdel Nikfarjam, Ralf Rothenberger, Frank Neumann 0001, Tobias Friedrich 0001. 938-945 [doi]
• First Improvement Hill Climber with Linkage Learning - on Introducing Dark Gray-Box Optimization into Statistical Linkage Learning Genetic AlgorithmsMichal Witold Przewozniczek, Renato Tinós, Marcin Michal Komarnicki. 946-954 [doi]
• To slide or not to slide? Moving along fitness levels and preserving the gene subsets diversity in modern evolutionary computationMichal Witold Przewozniczek, Marcin Michal Komarnicki. 955-962 [doi]
• On the Suitability of Representations for Quality Diversity Optimization of ShapesLudovico Scarton, Alexander Hagg. 963-971 [doi]
• Accelerating Evolution Through Gene Masking and Distributed SearchHormoz Shahrzad, Risto P. Miikkulainen. 972-980 [doi]
• Genetic Algorithm with Linkage LearningRenato Tinós, Michal Przewozniczek, Darrell Whitley, Francisco Chicano. 981-989 [doi]
• How the Move Acceptance Hyper-Heuristic Copes With Local Optima: Drastic Differences Between Jumps and CliffsBenjamin Doerr, Arthur Dremaux, Johannes F. Lutzeyer, Aurélien Stumpf. 990-999 [doi]
• How Well Does the Metropolis Algorithm Cope With Local Optima?Benjamin Doerr, Taha El Ghazi El Houssaini, Amirhossein Rajabi, Carsten Witt. 1000-1008 [doi]
• Quality-diversity in dissimilarity spacesSteve Huntsman. 1009-1018 [doi]
• Bayesian Quality Diversity Search with Interactive IlluminationPaul Kent, Jürgen Branke. 1019-1026 [doi]
• Analysis of a Pairwise Dominance Coevolutionary Algorithm And DefendItPer Kristian Lehre, Mario Hevia Fajardo, Jamal Toutouh, Erik Hemberg, Una-May O'Reilly. 1027-1035 [doi]
• Effective Parallelization of the Vehicle Routing ProblemRajesh Pandian Muniasamy, Somesh Singh 0001, Rupesh Nasre, N. S. Narayanaswamy. 1036-1044 [doi]
• How the Morphology Encoding Influences the Learning Ability in Body-Brain Co-OptimizationFederico Pigozzi, Federico Julian Camerota Verdù, Eric Medvet. 1045-1054 [doi]
• Enhanced Strongly typed Genetic Programming for Algorithmic TradingEvangelia Christodoulaki, Michael Kampouridis, Maria Kyropoulou. 1055-1063 [doi]
• Reducing Overparameterization of Symbolic Regression Models with Equality SaturationFabricio Olivetti de França, Gabriel Kronberger. 1064-1072 [doi]
• Probabilistic Lexicase SelectionLi Ding 0010, Edward R. Pantridge, Lee Spector. 1073-1081 [doi]
• Divide and conquer: Using single objective dispatching rules to improve convergence for multi-objective optimisationMarko Durasevic, Francisco Javier Gil Gala, Domagoj Jakobovic. 1082-1090 [doi]
• HOTGP - Higher-Order Typed Genetic ProgrammingMatheus Campos Fernandes, Fabrício Olivetti de França, Emílio Francesquini. 1091-1099 [doi]
• Comparing the expressive power of Strongly-Typed and Grammar-Guided Genetic ProgrammingAlcides Fonseca, Diogo Poças. 1100-1108 [doi]
• Down-Sampled Epsilon-Lexicase Selection for Real-World Symbolic Regression ProblemsAlina Geiger, Dominik Sobania, Franz Rothlauf. 1109-1117 [doi]
• Genetic programming for the vehicle routing problem with zone-based pricingFrancisco Javier Gil Gala, Sezin Afsar, Marko Durasevic, Juan José Palacios 0001, Murat Afsar. 1118-1126 [doi]
• Mini-Batching, Gradient-Clipping, First- versus Second-Order: What Works in Gradient-Based Coefficient Optimisation for Symbolic Regression?Joe Harrison, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman. 1127-1136 [doi]
• Grammar-guided Linear Genetic Programming for Dynamic Job Shop SchedulingZhixing Huang, Yi Mei 0001, Fangfang Zhang 0003, Mengjie Zhang 0001. 1137-1145 [doi]
• Fully Autonomous Programming with Large Language ModelsVadim Liventsev, Anastasiia Grishina, Aki Härmä, Leon Moonen. 1146-1155 [doi]
• A General Purpose Representation and Adaptive EA for Evolving GraphsEric Medvet, Simone Pozzi, Luca Manzoni. 1156-1164 [doi]
• An Investigation of Geometric Semantic GP with Linear ScalingGiorgia Nadizar, Fraser Garrow, Berfin Sakallioglu, Lorenzo Canonne, Sara Silva, Leonardo Vanneschi. 1165-1174 [doi]
• Solving Novel Program Synthesis Problems with Genetic Programming using Parametric PolymorphismEdward R. Pantridge, Thomas Helmuth. 1175-1183 [doi]
• Fast and Efficient Local-Search for Genetic Programming Based Loss Function LearningChristian Raymond, Qi Chen, Bing Xue 0001, Mengjie Zhang 0001. 1184-1193 [doi]
• A Double Lexicase Selection Operator for Bloat Control in Evolutionary Feature Construction for RegressionHengzhe Zhang, Qi Chen, Bing Xue 0001, Wolfgang Banzhaf, Mengjie Zhang 0001. 1194-1202 [doi]
• On Evolvability and Behavior Landscapes in Neuroevolutionary Divergent SearchBruno Gasperov, Marko Durasevic. 1203-1211 [doi]
• Understanding the Synergies between Quality-Diversity and Deep Reinforcement LearningBryan Lim, Manon Flageat, Antoine Cully. 1212-1220 [doi]
• The Quality-Diversity Transformer: Generating Behavior-Conditioned Trajectories with Decision TransformersValentin Macé, Raphaël Boige, Félix Chalumeau, Thomas Pierrot, Guillaume Richard, Nicolas Perrin-Gilbert. 1221-1229 [doi]
• Guiding the Exploration of the Solution Space in Walking Robots Through Growth-Based Morphological DevelopmentMartín Naya-Varela, Andrés Faiña, Richard J. Duro. 1230-1238 [doi]
• Stable and Sample-Efficient Policy Search for Continuous Control via Hybridizing Phenotypic Evolutionary Algorithm with the Double Actors Regularized CriticsThai Huy Nguyen, Ngoc Hoang Luong. 1239-1247 [doi]
• Learning to Act through Evolution of Neural Diversity in Random Neural NetworksJoachim Winther Pedersen, Sebastian Risi. 1248-1256 [doi]
• Fast Evolutionary Neural Architecture Search by Contrastive Predictor with Linear RegionsYameng Peng, Andy Song, Vic Ciesielski, Haytham M. Fayek, Xiaojun Chang. 1257-1266 [doi]
• Channel Configuration for Neural Architecture: Insights from the Search SpaceSarah L. Thomson, Gabriela Ochoa, Nadarajen Veerapen, Krzysztof Michalak. 1267-1275 [doi]
• MPENAS: Multi-fidelity Predictor-guided Evolutionary Neural Architecture Search with Zero-cost ProxiesJinglue Xu, Suryanarayanan N. A. V., Hitoshi Iba. 1276-1285 [doi]
• Tomographic Reconstruction with Search Space ExpansionMohammad Majid al-Rifaie, Tim Blackwell. 1286-1293 [doi]
• MOREA: a GPU-accelerated Evolutionary Algorithm for Multi-Objective Deformable Registration of 3D Medical ImagesGeorgios Andreadis, Peter A. N. Bosman, Tanja Alderliesten. 1294-1302 [doi]
• Using a Variational Autoencoder to Learn Valid Search Spaces of Safely Monitored Autonomous Robots for Last-Mile DeliveryPeter J. Bentley, Soo Ling Lim, Paolo Arcaini, Fuyuki Ishikawa. 1303-1311 [doi]
• Multi-Objective Seed Curve Optimization for Coverage Path Planning in Precision FarmingLukas Bostelmann-Arp, Christoph Steup, Sanaz Mostaghim. 1312-1320 [doi]
• EA-based ASV Trajectory Planner for Detecting Cyanobacterial Blooms in FreshwaterGonzalo Carazo-Barbero, Eva Besada-Portas, José Luis Risco-Martín, José Antonio López Orozco. 1321-1329 [doi]
• Computing Star Discrepancies with Numerical Black-Box Optimization AlgorithmsFrançois Clément, Diederick Vermetten, Jacob de Nobel, Alexandre D. Jesus, Luís Paquete, Carola Doerr. 1330-1338 [doi]
• Trade-off Between Robustness and Worst-Case Performance in Min-Max OptimizationHinata Edo, Yoshiki Miyauchi, Atsuo Maki, Youhei Akimoto. 1339-1347 [doi]
• Evolving Reinforcement Learning Environment to Minimize Learner's Achievable Reward: An Application on Hardening Active Directory SystemsDiksha Goel, Aneta Neumann, Frank Neumann 0001, Hung Nguyen 0004, Mingyu Guo. 1348-1356 [doi]
• Multi-Objective Multi-Gene Genetic Programming for the Prediction of Leakage in Water Distribution NetworksMatthew Hayslep, Edward C. Keedwell, Raziyeh Farmani. 1357-1364 [doi]
• Combined Layout Optimization of Wind Farm and Cable Connection on Complex Terrain Using a Genetic AlgorithmDaiki Kiribuchi, Ryoko Hatakeyama, Tomoshi Otsuki, Tatsuya Yoshioka, Kana Konno, Takumi Matsuda. 1365-1373 [doi]
• Optimizing Dispatching Strategies for Semiconductor Manufacturing Facilities with Genetic ProgrammingBenjamin Kovács, Pierre Tassel, Martin Gebser. 1374-1382 [doi]
• ChatGPT and Other Large Language Models as Evolutionary Engines for Online Interactive Collaborative Game DesignPier Luca Lanzi, Daniele Loiacono. 1383-1390 [doi]
• Vertical-Axis Wind Turbine Design Using Surrogate-assisted Optimization with Physical Experiments In-loopMatthew Lette, Kamrul Hasan Rahi, Hemant Kumar Singh, Tapabrata Ray. 1391-1399 [doi]
• Evolutionary Approach to Recommender Systems Improvement by Directory of Products OptimizationPiotr Lipinski. 1400-1408 [doi]
• Learning Emergency Medical Dispatch Policies Via Genetic ProgrammingJordan MacLachlan, Yi Mei 0001, Fangfang Zhang 0003, Mengjie Zhang 0001, Jessica Signal. 1409-1417 [doi]
• Evolving Flying Machines in Minecraft Using Quality DiversityAlejandro Medina, Melanie Richey, Mark Mueller, Jacob Schrum. 1418-1426 [doi]
• Effective EEG Feature Selection for Interpretable MDD (Major Depressive Disorder) ClassificationVojtech Mrazek, Soyiba Jawed, Muhammad Arif, Aamir Saeed Malik. 1427-1435 [doi]
• Diversity Optimization for the Detection and Concealment of Spatially Defined Communication NetworksAneta Neumann, Sharlotte Gounder, Xiankun Yan, Gregory Sherman, Benjamin Campbell, Mingyu Guo, Frank Neumann 0001. 1436-1444 [doi]
• A Fast Multi-objective Evolutionary Approach for Designing Large-Scale Optical Mode SorterAnnibale Panichella, Giuseppe Di Domenico. 1445-1453 [doi]
• Comparing Metaheuristic Optimization Algorithms for Ambulance Allocation: An Experimental Simulation StudyMagnus Eide Schjølberg, Nicklas Paus Bekkevold, Xavier F. C. Sánchez-Díaz, Ole Jakob Mengshoel. 1454-1463 [doi]
• Towards Evolutionary Control Laws for Viability ProblemsAlberto Tonda, Isabelle Alvarez, Sophie Martin, Giovanni Squillero, Evelyne Lutton. 1464-1472 [doi]
• Scheduling Multi-Resource Satellites using Genetic Algorithms and Permutation Based RepresentationsDarrell Whitley, Ozeas Quevedo de Carvalho, Mark Roberts, Vivint Shetty, Piyabutra Jampathom. 1473-1481 [doi]
• Automatic Hyper-Heuristic to Generate Heuristic-based Adaptive Sliding Mode Controller Tuners for Buck-Boost ConvertersDaniel F. Zambrano-Gutierrez, Jorge Mario Cruz-Duarte, Herman Castañeda. 1482-1489 [doi]
• Searching for Quality: Genetic Algorithms and Metamorphic Testing for Software Engineering MLLeonhard Applis, Annibale Panichella, Ruben Marang. 1490-1498 [doi]
• Automated Repair of Unrealisable LTL Specifications Guided by Model CountingMatías Brizzio, Maxime Cordy, Mike Papadakis, César Sánchez, Nazareno Aguirre, Renzo Degiovanni. 1499-1507 [doi]
• Learning by Viewing: Generating Test Inputs for Games by Integrating Human Gameplay Traces in NeuroevolutionPatric Feldmeier, Gordon Fraser 0001. 1508-1517 [doi]
• Search-Based Test Generation Targeting Non-Functional Quality Attributes of Android AppsTeklit Gereziher, Selam Gebrekrstos, Gregory Gay 0002. 1518-1526 [doi]
• Adaptive Search-based Repair of Deep Neural NetworksDavide Li Calsi, Matias Duran, Thomas Laurent 0003, Xiao-Yi Zhang 0005, Paolo Arcaini, Fuyuki Ishikawa. 1527-1536 [doi]
• Fixed Parameter Multi-Objective Evolutionary Algorithms for the W-Separator ProblemSamuel Baguley, Tobias Friedrich 0001, Aneta Neumann, Frank Neumann 0001, Marcus Pappik, Ziena Zeif. 1537-1545 [doi]
• Runtime Analysis of Quality Diversity AlgorithmsJakob Bossek, Dirk Sudholt. 1546-1554 [doi]
• Fourier Analysis Meets Runtime Analysis: Precise Runtimes on PlateausBenjamin Doerr, Andrew James Kelley. 1555-1564 [doi]
• Tight Runtime Bounds for Static Unary Unbiased Evolutionary Algorithms on Linear FunctionsCarola Doerr, Duri Andrea Janett, Johannes Lengler. 1565-1574 [doi]
• Calculating lexicase selection probabilities is NP-HardEmily L. Dolson. 1575-1583 [doi]
• Analysis of (1+1) EA on LeadingOnes with ConstraintsTobias Friedrich 0001, Timo Kötzing, Aneta Neumann, Frank Neumann 0001, Aishwarya Radhakrishnan. 1584-1592 [doi]
• How Fitness Aggregation Methods Affect the Performance of Competitive CoEAs on Bilinear ProblemsMario Alejandro Hevia Fajardo, Per Kristian Lehre. 1593-1601 [doi]
• Comma Selection Outperforms Plus Selection on OneMax with Randomly Planted OptimaJoost Jorritsma, Johannes Lengler, Dirk Sudholt. 1602-1610 [doi]
• Runtime Analysis with Variable CostPer Kristian Lehre, Andrew M. Sutton. 1611-1618 [doi]
• Self-adaptation Can Help Evolutionary Algorithms Track Dynamic OptimaPer Kristian Lehre, Xiaoyu Qin. 1619-1627 [doi]
• Analysing Equilibrium States for Population DiversityJohannes Lengler, Andre Opris, Dirk Sudholt. 1628-1636 [doi]