Application of hybrid genetic algorithm in optimization formula system
Jie Cheng, Yun Yang
Abstract
Jie Cheng, Yun Yang
Abstract
In order to find the best method to solve the problem of multi-objective function optimization,we propose a hybrid genetic algorithm which combines genetic algorithm with complex method.The algorithm first uses genetic algorithm to get an initial population and replaces original feasible points by the computation results of complex method, then it uses genetic algorithm to find the optimal solution. When termination conditions are met, complex method is used to get the final result. Experiment has been conducted to validate the propoesd algorithm by taking optimization formula system as an example. The results shows that we got the best percentage of formula,and the propoesd algorithm is more accurate than the simple genetic algorithm and complex method ,which has a good prospect of application in the area of optimization design.
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In order to find the best method to solve the problem of multi-objective function optimization,we propose a hybrid genetic algorithm which combines genetic algorithm with complex method.The algorithm first uses genetic algorithm to get an initial population and replaces original feasible points by the computation results of complex method, then it uses genetic algorithm to find the optimal solution. When termination conditions are met, complex method is used to get the final result. Experiment has been conducted to validate the propoesd algorithm by taking optimization formula system as an example. The results shows that we got the best percentage of formula,and the propoesd algorithm is more accurate than the simple genetic algorithm and complex method ,which has a good prospect of application in the area of optimization design.
Key concepts: Meta-optimization, Genetic algorithm, Population-based incremental learning, Cultural algorithm, Computer science, Algorithm, Mathematical optimization, Computation