Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms
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The present study develops a new genetic algorithm, Gene Controlling Genetic Algorithm (GCGA) for optimization design. In performing engineering design optimization, this genetic algorithm intends to maximize the utilization of the existing information such as practical design knowledge, existing relationships of design parameters and engineering experience. With GCGA, design optimization process becomes much efficient. Application of GCGA in optimizing an electric locomotive transformer design is provided in this study. Detailed procedures of optimization with GCGA are presented. In comparing with the traditional genetic algorithm such as EPGA, as indicated in the study, GCGA provides a global convergent solution with higher stability and faster speed. It is demonstrated that GCGA is an algorithm specifically suitable for optimizing complex designs in engineering practice.