Optimization of noisy fitness functions by means of genetic algorithms using history of search with test of estimation
Optimization of noisy fitness functions by means of genetic algorithms using history of search with test of estimation
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DOI：
10.1109/cec.2002.1006261
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发表时间：
2000-09
期刊：
影响因子：
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通讯作者：
Y. Sano;H. Kita
中科院分区：
文献类型：
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作者：
Y. Sano;H. Kita
用遗传算法讨论了不确定函数的优化问题。在此类GAs的实际应用中，可能的适应度评估数量非常有限。作者提出了一种利用搜索历史的遗传算法（基于记忆的适应度评估遗传算法：MFEGA），以减少这类应用中适应度评估的数量。然而，由于MFEGA使用搜索历史来估计适应度值，当最优点位于人口覆盖区域之外时，MFEGA面临困难。为了克服上述问题，作者提出了经检验的MFEGA，即对估计的适应度值进行有效性检验的MFEGA的扩展。数值实验表明，即使原始的MFEGA失效，该方法也比传统的采样适应度遗传算法要好几倍。
The authors discuss optimization of functions with uncertainty by means of genetic algorithms (GAs). In practical application of such GAs, the possible number of fitness evaluations is quite limited. The authors have proposed a GA utilizing history of search (Memory-based Fitness Evaluation GA: MFEGA) so as to reduce the number of fitness evaluations for such applications of GAs. However, it is also found that the MFEGA faces difficulty when the optimum resides outside of the region where population covers because the MFEGA uses the history of search for estimation of fitness values. The authors propose the tested-MFEGA, an extension of the MFEGA that tests validity of the estimated fitness value so as to overcome the aforesaid problem. Numerical experiments show that the proposed method outperforms a conventional GA of sampling fitness values several times even when the original MFEGA fails.