Work

A Detailed Experimental Analysis of Evolutionary Diversity Optimization for OneMinMax

Year: 2024

Type: conference-paper

Abstract: Real-world optimization problems often require finding not only one good solution, but a diverse set of good solutions. Evolutionary algorithms (EAs) have been shown to suit well for such a task. However our theoretical understanding of their behavior remains unsatisfying, especially in the multi-objective domain.

Source: Genetic and Evolutionary Computation Conference (GECCO)

DOI: 10.1145/3638529.3654082

Authors: Denis Antipov, Aneta Neumann, Frank Neumann

Institution: The University of Adelaide

Language: English

Topic: Advanced Multi-Objective Optimization Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Australian Research Council

Awards: Evolutionary diversity optimisation, Bio-inspired Computing for Problems with Chance Constraints