A self adaptive hybrid genetic algorithm

Abstract

This paper presents a self-adaptive hybrid genetic algorithm (SAHGA) and compares its performance to a non-adaptive hybrid genetic algorithm (NAHGA) and the simple genetic algorithm (SGA) on two multi-modal test functions with complex geometry. The SAHGA is shown to be far more robust than the NAHGA, providing fast and reliable convergence across a broad range of parameter settings. For the most complex test function, the SAHGA required over 75% fewer function evaluations than the SGA to identify the optimal solution.