Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Culturizing Differential Evolution for Constrained Optimization
ENC '04 Proceedings of the Fifth Mexican International Conference in Computer Science
Optimal placement of active members for truss structure using genetic algorithm
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
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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.