Critical control of a genetic algorithm
Rapha\"el Cerf
TL;DR
This paper proposes a heuristic for controlling mutation probability and population size in genetic algorithms, inspired by statistical mechanics and the concept of critical states.
Contribution
It introduces a novel heuristic approach to optimize genetic algorithm parameters based on theoretical insights.
Findings
Improved control over genetic algorithm performance
Potential for enhanced convergence efficiency
Framework grounded in statistical mechanics principles
Abstract
Based on speculations coming from statistical mechanics and the conjectured existence of critical states, I propose a simple heuristic in order to control the mutation probability and the population size of a genetic algorithm.
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Taxonomy
TopicsEvolutionary Algorithms and Applications · Gene Regulatory Network Analysis · Metaheuristic Optimization Algorithms Research