A Grid-based Genetic Algorithm for Multimodal Real Function Optimization

A novel genetic algorithm called GGA (Grid-based Genetic Algorithm) is presented to improve the optimization of multimodal real functions. The search space is discretized using a grid, making the search process more efficient and faster. An integer-real vector codes the genotype and a GA is used for evolving the population. The integer part allows us to explore the search space and the real part to exploit the best solutions. A comparison with a standard GA is performed using typical benchmarking multimodal functions from the literature. In all the tested problems, the proposed algorithm equals or outperforms the standard GA.