A Self-Organizing Genetic Algorithm With a Eugenic Strategy

A Genetic Algorithm (GA) is general optimization technique suitable for solving nonlinear, multi-constraints, and combinatorial optimization problems. However, it may be slow in converging of failing to reach the global optimum. A novel strategy called Self-Organizing Eugenics Strategy (SOES) is proposed to overcome these shortcomings. In the proposed algorithm, a simplified adaptive resonance theory neural network (ART) is embedded to generate schemata, and the simulated annealing algorithm (SA) is applied to guiding the search toward an optimal solution. To illustrate its improved performance, the method is used to solve an optimization problem. Based on the results of experiments, the method demonstrates a better performance than the traditional GA and other genetic operations.