- The following papers have been nominated for a Best Paper Award.
- For each Best Paper (one per track, or group of small tracks), the final choice will be made by GECCO attendees.
List of Best Paper Nominations
| Track | Title | Authors |
|---|---|---|
| RWA | Automatic Hyper-Heuristic to Generate Heuristic-based Adaptive Sliding Mode Controller Tuners for Buck-Boost Converters | Daniel Zambrano-Gutierrez (Tecnológico de Monterrey), Jorge Cruz-Duarte (Tecnológico de Monterrey), Herman Castañeda (Tecnológico de Monterrey) |
| CS | Selection for short-term empowerment accelerates the evolution of homeostatic neural cellular automata | Caitlin Grasso (University of Vermont), Josh Bongard (University of Vermont) |
| GA | Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization | Robert Lange (Technical Univ. Berlin), Tom Schaul (Google DeepMind), Yutian Chen (Google DeepMind), Chris Lu (University of Oxford), Tom Zahavy (Google DeepMind), Valentin Dalibard (Google DeepMind), Sebastian Flennerhag (Google DeepMind) |
| GECH | Quality-diversity in dissimilarity spaces | Steve Huntsman (STR) |
| ACO-SI | Pid-Inspired Modifications in Response Threshold Models In Swarm Intelligent Systems | Maryam Kebari (University of Central Florida), Annie Wu (University of Central Florida), H. Mathias (University of Wisconsin-La Crosse) |
| ECOM | New Knowledge about the Elementary Landscape Decomposition for Solving the Quadratic Assignment Problem | Xabier Benavides (Basque Center for Applied Mathematics, University of the Basque Country), Josu Ceberio (University of the Basque Country), Leticia Hernando (University of the Basque Country), Jose Antonio Lozano (Basque Center for Applied Mathematics, University of the Basque Country) |
| SBSE | Learning by Viewing: Generating Test Inputs for Games by Integrating Human Gameplay Traces in Neuroevolution | Patric Feldmeier (University of Passau), Gordon Fraser (University of Passau) |
| ENUM | Evolutionary Mixed-Integer Optimization with Explicit Constraints | Yuan Hong (Dalhousie University), Dirk Arnold (Dalhousie University) |
| GP | Grammar-guided Linear Genetic Programming for Dynamic Job Shop Scheduling | Zhixing Huang (Victoria University of Wellington), Yi Mei (Victoria University of Wellington), Fangfang Zhang (Victoria University of Wellington), Mengjie Zhang (Victoria University of Wellington) |
| GP | Fast and Efficient Local-Search for Genetic Programming Based Loss Function Learning | Christian Raymand (Victoria University of Wellington), Qi Chen (Victoria University of Wellington), Bing Xue (Victoria University of Wellington), Mengjie Zhang (Victoria University of Wellington) |
| ENUM | CMA-ES with Learning Rate Adaptation: Can CMA-ES with Default Population Size Solve Multimodal and Noisy Problems? | Masahiro Nomura (Tokyo Institute of Technology), Youhei Akimoto (University of Tsukuba, RIKEN AIP), Isao Ono (Tokyo Institute of Technology) |
| ENUM | Using Affine Combinations of BBOB Problems for Performance Assessment | Diederick Vermetten (Leiden University), Furong Ye (Leiden University), Carola Doerr (Sorbonne Universite, CNRS) |
| RWA | Multi-Objective Multi-Gene Genetic Programming for the Prediction of Leakage in Water Distribution Networks | Matthew Hayslep (University of Exeter), Edward Keedwell (University of Exeter), Raziyeh Farmani (University of Exeter) |
| ACO-SI | The Impact of Morphological Diversity in Robot Swarms | Geoff Nitschke (UCT), Sindiso Mkhatshwa (UCT) |
| EML | Covariance Matrix Adaptation MAP-Annealing | Matthew Fontaine (University of Southern California), Stefanos Nikolaidis (University of Southern California) |
| ECOM | Q-Learning Ant Colony Optimization supported by Deep Learning for Target Set Selection | Jairo Enrique Ramírez Sánchez (Tecnologico de Monterrey), Camilo Chacón Sartori (IIIA-CSIC), Christian Blum (IIIA-CSIC) |
| NE | Fast Evolutionary Neural Architecture Search by Contrastive Predictor with Linear Regions | Yameng Peng (RMIT University), Andy Song (RMIT University), Vic Ciesielski (RMIT University), Haytham Fayek (RMIT University), Xiaojun Chang (University of Technology Sydney) |
| EML | Fuzzy-UCS Revisited: Self-Adaptation of Rule Representations in Michigan-Style Learning Fuzzy-Classifier Systems | Hiroki Shiraishi (Yokohama National University), Yohei Hayamizu (The State University of New York at Binghamton), Tomonori Hashiyama (The University of Electro-Communications) |
| GP | Fully Autonomous Programming with Large Language Models | Vadim Liventsev (Technical University of Eindhoven, Philips Research), Anastasiia Grishina (Simula Research Laboratory, University of Oslo), Aki Härmä (Philips Research), Leon Moonen (Simula Research Laboratory, BI Norwegian Business School) |
| CS | MAP-Elites with Descriptor-Conditioned Gradients and Archive Distillation into a Single Policy | Maxence Faldor (Imperial College London), Félix Chalumeau (InstaDeep), Manon Flageat (Imperial College London), Antoine Cully (Imperial College London) |
| GA | First Improvement Hill Climber with Linkage Learning -- on Introducing Dark Gray-Box Optimization into Statistical Linkage Learning Genetic Algorithms | Michal Przewozniczek (Wroclaw University of Science and Technology), Renato Tinós (University of São Paulo), Marcin Komarnicki (Wroclaw University of Science and Technology) |
| Theory | Calculating lexicase selection probabilities is NP-Hard | Emily Dolson (Michigan State University) |
| EML | Interactive Latent Diffusion Model | Mathurin Videau (Meta, Inria Saclay–Île-de-France), Nickolai Knizev (Meta), Alessandro Leite (Inria Saclay–Île-de-France), Marc Schoenauer (Inria Saclay–Île-de-France), Olivier Teytaud (Meta) |
| EMO | Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness | Arnaud Liefooghe (University of Lille, Inria), Gabriela Ochoa (University of Stirling), Sébastien Verel (Université du Littoral Côte d'Opale), Bilel Derbel (University of Lille, Inria) |
| EMO | On the Unbounded External Archive and Population Size in Preference-based Evolutionary Multi-objective Optimization Using a Reference Point | Ryoji Tanabe (Yokohama National University) |
| EMO | Many-objective (Combinatorial) Optimization is Easy | Arnaud Liefooghe (University of Lille, Inria), Manuel López-Ibáñez (University of Manchester) |