一种基于种群熵抽样的小种群自适应变异遗传算法
首发时间:2009-01-13
摘要:针对交互式进化计算(IEC)对进化算法提出的在种群规模小的情况下仍能保持优秀搜索性能的特殊要求,分析了作为进化算法的遗传算法当前研究的不足,设计了一种基于种群熵抽样方法来自适应调节种群多样性的有效变异策略,并进而提出了一种更加 高效的小种群遗传算法。该算法在采用赌轮选择和单点交叉的情况下,能够更有效的避免陷入局部搜索,以更高的精度快速收敛逼近 全局最优解,从而能很好的满足IEC 的应用要求。对低维与高维多模态标杆函数的对比仿真实验表明了算法的有效性和稳定性。
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A Novel Small-Population Genetic Algorithm based on
Abstract:The application of Interactive Evolutionary Computation (IEC) requires that corresponding evolutionary algorithms should still have effective and stable performance with small population. The corresponding study of genetic algorithms as evolutionary algorithm is analyzed. A novel mutation strategy based on population entropy sampling to adjust the population diversity intentionally and adaptively is designed, and a new adaptive genetic algorithm with small population is proposed. The proposed algorithm integrating roulette wheel selection and one-point crossover can avoid the premature convergence more effectively and obtain more precise global optimal solutions with fast convergence speed, which makes the proposed algorithm suitable for the application of IEC. Seven multimodal benchmark functions are used to test the performance of the proposed algorithm and the results show that the new algorithm is more effective and stable.
Keywords: adaptive genetic algorithm population entropy multimodal function small population
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