Hybrid Model of Genetic Algorithm and Cultural Algorithms for Optimization Problem
To solve constrained optimization problems, we propose to integrate genetic algorithm (GA) and cultural algorithms (CA) to develop a hybrid model (HMGCA). In this model, GA’s selection and crossover operations are used in CA’s population space. A direct comparison-proportional method is employed in GA’s selections to keep a certain proportion of infeasible but better (with higher fitness) individuals, which is beneficial to the optimization. Elitist preservation strategy is also used to enhance the global convergence. GA’s mutation is replaced by CA based mutation operation which can attract individuals to move to the semi-feasible and feasible region of the optimization problem to improve search direction in GA. Thus it is possible to enhance search ability and to reduce computational cost. A simulation example shows the effectiveness of the proposed approach.
KeywordsGenetic Algorithm Hybrid Model Constrain Optimization Problem Real Code Genetic Algorithm Belief Space
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