A Novel Mechanism for Efficient the Search Optimization of Genetic Algorithm

• Main Authors: Chen-Fang Tsai, Shin-Li Lu
• Format: Article
• Language: English
• Published: Atlantis Press 2016-01-01
• Series: International Journal of Computational Intelligence Systems
• Subjects: Genetic algorithm; Adaptive Crossover; Adaptive Mutation
• Online Access: https://www.atlantis-press.com/article/25868679/view

This paper proposes a Social Genetic Algorithm (SGA) that includes a transformation function that has ability to improve search efficiency. The SGA is different from the Traditional Genetic Algorithm (TGA) approaches, as it allows refinement of the TGA parameters for the selections of operators in each generation with two functions: optimization of crossover rate and optimization of mutation rate. In this paper, a new function that optimizes gene relationship has been introduced to advance the evolution capability and flexibility of SGA in searching complex and large solution space. Our proposed approach has been evaluated using simulation models. The simulation results have shown that SGA outperforms TGA in improving search efficiency. The contribution of the proposed approach is a dynamic and adaptive methodology, which has ability to improve efficiency.