In spite of the recent quick growth of the Evolutionary Multi-objective Optimization (EMO) research field, there has been few trials to adapt the general variation operators to the particular context of the quest for the Pareto-optimal set. The only exceptions are some mating restrictions that take in account the distance between the potential mates - but contradictory conclusions have been reported. This paper introduces a particular mating restriction for Evolutionary Multi-objective Algorithms, based on the Pareto dominance relation: the partner of a non-dominated individual will be preferably chosen among the individuals of the population that it dominates. Coupled with the BLX crossover operator, two different ways of generating offspring are proposed. This recombination scheme is validated within the well-known NSGA-II framework on three bi-objective benchmark problems and one real-world bi-objective constrained optimization problem. An acceleration of the progress of the population toward the Pareto set is observed on all problems.
A Hybrid Evolutionary Algorithm Based on Solution Merging for the Longest Arc-Preserving Common Subsequence Problem
Artificial Intelligence Research Institute (IIIA-CSIC)（人工智能研究所 (IIIA-CSIC)）；Universitat Politècnica de Catalunya (BarcelonaTech)（加泰罗尼亚理工大学 (BarcelonaTech)）
Autonomous Algorithm Discovery for Ptychography via Evolutionary LLM Reasoning
Advanced Photon Source, Argonne National Laboratory, Lemont, IL, USA（阿贡国家实验室先进光子源）；Department of Materials Science and NanoEngineering, Rice University, Houston, TX, USA（材料科学与纳米工程系，德克萨斯大学里士满分校）
Evolutionary Architecture Search through Grammar-Based Sequence Alignment
Carlos III University of Madrid（马德里卡洛斯三世大学）；University of Edinburgh（爱丁堡大学）；Instituto de Investigación Sanitaria Gregorio Marañón（Gregorio Marañón卫生研究院）；Helmholtz-Zentrum Berlin für Materialien und Energie（柏林材料与能源研究中心）；ELLIS Institute Tübingen（图宾根ELLIS研究所）；University of Glasgow（格拉斯哥大学）
Node Preservation and its Effect on Crossover in Cartesian Genetic Programming
Department of Computer Science（计算机科学系）；BEACON Center for the Study of Evolution in Action Michigan State University, East Lansing, MI 48824, USA（进化机制行动研究中心 密歇根州立大学 埃文斯兰斯）
Efficient Evolutionary Search Over Chemical Space with Large Language Models
Georgia Institute of Technology（佐治亚理工学院）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；Massachusetts Institute of Technology（麻省理工学院）；University of Wuppertal（伍珀塔尔大学）；Deep Principle Inc.（Deep Principle公司）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Cornell University（康奈尔大学）；Université de Montréal（蒙特利尔大学）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）
Coronavirus Optimization Algorithm: A Success-History Adaptive Evolutionary Framework with Archive-Assisted Search and Stagnation Recovery for Global Optimization
