New Approach of Self-Adaptive Simulated Binary Crossover-Elitism in Genetic Algorithms for Numerical Function Optimization - Fanggidae, Adriana; Catur Prasetyo, Muhammad Iqrom; Triwahyuadi Polly, Yulianto; Boru, Meiton (International Journal of Intelligent Systems and Applications in Engineering, 2024)
One of the critical evolutionary operators in genetic algorithms (GAs) is crossover. Simulated Binary Crossover (SBX) is a commonly employed crossover operator in GA for real-valued encoding. Self-Adaptive SBX introduces a distribution index parameter that is updated in each generation, enabling the offspring solution distance to be independent of the parent solution distance. During evolution, the extinction of the fittest individuals is possible, and elitism is employed to prevent such extinction, thereby preserving the quality of the offspring. This research proposes GA with Self-Adaptive SBX-Elitism to enhance the performance of GA with Self-Adaptive SBX. The performance of GA with Self-Adaptive SBX-Elitism and GA with Self-Adaptive SBX is tested on ten benchmark functions. The test results on ten populations in dimensions ten, twenty, and thirty indicate that GA with Self-Adaptive SBX-Elitism can reduce the average relative error by 99.99%, with an average computation time that is 19.40% faster compared to GA with Self-Adaptive SBX. GA with Self-Adaptive SBX-Elitism performs well across twenty populations in all test dimensions.