The Hot off the Press track offers authors of recent papers the opportunity to present their work to the GECCO community, both by giving a talk on one of the three main days of the conference and by having a 2-page abstract appear in the proceedings companion, in which also the workshop papers, late-breaking abstracts, and tutorials appear. For more information see the Call for HOPs.
| Title | Authors |
|---|---|
| Scalable Speed-ups for the SMS-EMOA from a Simple Aging Strategy | Mingfeng Li (Harbin Institute of Technology |
| Proven Approximation Superiority of SPEA2 over NSGA-II | Yasser Alghouass (École Polytechnique) |
| BONO-Bench: A Comprehensive Test Suite for Bi-objective Numerical Optimization with Traceable Pareto Sets | Lennart Schäpermeier (University of Münster) |
| Speeding Up Hyper-Heuristics With Markov-Chain Operator Selection and the Only-Worsening Acceptance Operator | Abderrahim Bendahi (École Polytechnique) |
| Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings | Rong-Xi Tan (Nanjing University) |
| Theoretical Analysis of Evolutionary Algorithms with Quality Diversity for a Classical Path Planning Problem | Duc-Cuong Dang (University of Passau) |
| Hot of the Press: Tight Runtime Guarantees From Understanding the Population Dynamics of the GSEMO Multi-Objective Evolutionary Algorithm | Benjamin Doerr (Laboratoire d’Informatique (LIX) |
| Migrant Resettlement by Evolutionary Multi-objective Optimization | Dan-Xuan Liu (Nanjing University) |
| Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes | TrailingZeros) Problem |
| A Theoretical Perspective on Why Stochastic Population Update Needs an Archive in Evolutionary Multi-objective Optimization | Shengjie Ren (Nanjing University) |
| Superior Runtime Guarantees for the MOEA/D Multi-Objective Optimizer via Weighted-Sum Decomposition | Danyang Zhang (Harbin Institute of Technology) |
| Why Popular MOEAs Are Popular: Proven Advantages in Approximating the Pareto Front | Mingfeng Li (Harbin Institute of Technology |
| Sequential Multi-Agent Dynamic Algorithm Configuration | Chen Lu (Nanjing University) |
| Random is Faster than Systematic Exploration in Multi-Objective Local Search | Zimin Liang (University of Birmingham) |
| The Runtime of Randomized Local Search on the Generalized Needle Problem | Benjamin Doerr (Laboratoire d'Informatique (LIX) |
| FEAT-KD: Learning Concise Representations for Single and Multi-Target Regression via TabNet Knowledge Distillation | Kei Sen Fong (National University of Singapore) |
| Towards a Rigorous Understanding of the Population Dynamics of the NSGA-III: Tight Runtime Bounds | Andre Opris (University of Passau) |
| The Variability of Evolvability: Properties of Dynamic Fitness Landscapes Determine how Phenotypic Variability Evolves | Csenge Petak (University of Vermont) |
| The Genomic Code: The Genome Instantiates a Generative Model of the Organism | Kevin J. Mitchell (Trinity College Dublin) |
| Evolutionary Algorithms Are Significantly More Robust to Noise When They Ignore It | Denis Antipov (Sorbonne University |
| Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms | Chao Bian (Nanjing University) |
| The First Theoretical Approximation Guarantees for the Non-Dominated Sorting Genetic Algorithm III (NSGA-III) | Renzhong Deng (Harbin Institute of Technology) |
| Hot of the Press: A First Runtime Analysis of NSGA-III on a Many-Objective Multimodal Problem: Provable Exponential Speedup via Stochastic Population Update | Andre Opris (University of Passau) |
| Improved Theoretically-Grounded Evolutionary Algorithms for Subset Selection with a Linear Cost Constraint | Dan-Xuan Liu (Nanjing University) |
| An Evolutionary Approach for the Computation of 𝜖-Locally Optimal Solutions for Multi-Objective Multimodal Optimization | Carlos Hernández Castellanos (IIMAS-UNAM) |
| Improved Runtime Guarantees for the SPEA2 Multi-Objective Optimizer | Benjamin Doerr (École Polytechnique |
| Pareto-Optimal Fronts for Benchmarking Symbolic Regression Algorithms | Kei Sen Fong (National University of Singapore) |