Substitution of the Fittest: A Novel Approach for Mitigating Disengagement in Coevolutionary Genetic Algorithms

2021/08/06 by Hugo Alcaraz‐Herrera, Alcaraz-Herrera, Hugo, John Cartlidge +1
Computer Science · #Advanced Multi-Objective Optimization Algorithms #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE)

paper · doi:10.48550/arxiv.2108.03156

openalex publication_date 2021/08/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We propose substitution of the fittest (SF), a novel technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms. The approach presented is domain-independent and requires no calibration. In a minimal domain, we perform a controlled evaluation of the ability to maintain engagement and the capacity to discover optimal solutions. Results demonstrate that the solution discovery performance of SF is comparable with other techniques in the literature, while SF also offers benefits including a greater ability to maintain engagement and a much simpler mechanism.

Related

• Fitness-based Adaptive Control of Parameters in Genetic Programming: Adaptive Value Setting of Mutation Rate and Flood Mechanisms
• Overcoming Binary Adversarial Optimisation with Competitive Coevolution
• A review of landmark articles in the field of co-evolutionary computing
• On Cooperative Coevolution and Global Crossover
• Using Dissortative Mating Genetic Algorithms to Track the Extrema of Dynamic Deceptive Functions