Temporal Heterogeneity Improves Speed and Convergence in Genetic Algorithms
This work addresses a specific optimization challenge for researchers and practitioners using genetic algorithms, though it appears incremental as it modifies an existing method.
The paper tackled the problem of improving genetic algorithms by introducing temporal heterogeneity, where crossover probability is inversely proportional to fitness, and found that this approach consistently improves search speed and convergence in solving N-Queens and Traveling Salesperson problems.
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.