Avoiding Premature Convergence to Local Optima with Adaptive Exploration for Genetic Algorithms

Genetic algorithm is a well-established optimization approach especially for black-box optimization systems. However, traditional genetic algorithms have been known to suffer from premature convergence to suboptimal regions and inability to escape local optima. This is particularly applicable for systems with many local optima. Genetic operators designed to tackle this issue by increasing population diversity are not always optimal for a wide variety of applications. We propose an adaptive multi-restart heuristic approach that encourages exploration of the search space as needed to improve accuracy and robustness of existing genetic algorithms. The decision to continue exploration for global optima or to converge to an already found optimal region is guided by statistical evidence via clustering of the existing best solutions from parallel islands. Empirical results demonstrate that our proposed adaptive exploration strategy boosts the performance of traditional genetic algorithms by enabling to escape local optima with higher accuracy and improved robustness.