Temporal Heterogeneity Improves Speed and Convergence in Genetic Algorithms

TL;DR

This paper demonstrates that introducing temporal heterogeneity in genetic algorithms, by adjusting crossover probabilities based on fitness, enhances search speed and convergence without prior parameter tuning, as shown in N-Queens and TSP problems.

Contribution

It introduces a novel method of temporal heterogeneity in genetic algorithms, where crossover probability varies with fitness, improving search efficiency.

Findings

Temporal heterogeneity improves search performance.

The method enhances convergence speed.

No prior parameter tuning is required.

Abstract

Genetic algorithms have been used in recent decades to solve a broad variety of search problems. These algorithms simulate natural selection to explore a parameter space in search of solutions for a broad variety of problems. In this paper, we explore the effects of introducing temporal heterogeneity in genetic algorithms. In particular, we set the crossover probability to be inversely proportional to the individual's fitness, i.e., better solutions change slower than those with a lower fitness. As case studies, we apply heterogeneity to solve the $N$-Queens and Traveling Salesperson problems. We find that temporal heterogeneity consistently improves search without having prior knowledge of the parameter space.