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The paper deals with the performance of genetic algorithm according to the analysis of control parameters, the evolution between different sub-populations, the interaction on best individual and, the expansion on the search interval, are analyzed, and an adaptive genetic algorithm is presented, which is multi-population parallel evolutionary and variable population size. It is proved by comparative experiments that the speed of convergence and the precision of the new algorithm are considerably improved, which avoid the premature convergence phenomenon of single-population evolutionary algorithm, and maintain the evolutionary stability of the best individuals, so it effectively makes up the, shortcomings of single-population and constant parameters, which don't overcome the premature phenomenon universally and so on. The results show that it is better than the traditional one both robustness and effectiveness of the algorithm. Therefore, the algorithm in practice has a broad application prospects.
Intelligent Systems (GCIS), 2010 Second WRI Global Congress on (Volume:1 )
Date of Conference: 16-17 Dec. 2010