Hybrid Population-Based Incremental Learning Using Real Codes
Hybrid Population-Based Incremental Learning Using Real Codes
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DOI：
10.1007/978-3-642-25566-3_28
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发表时间：
2011-01
影响因子：
1.8
通讯作者：
Sujin Bureerat
中科院分区：
文献类型：
--
作者：
Sujin Bureerat
本文提出了一种基于种群增量学习的混合进化算法（EA）和一些高效的局部搜索策略。开发了一个使用实码的简单PBIL。将演化方向算子和近似梯度算子集成到PBIL的主要过程中。提出了单目标全局优化方法。将所提出的混合盒约束优化算法的搜索性能与许多已建立和新开发的进化算法和元启发式算法进行了比较。在给定的优化设置下，所提出的混合优化器优于其他ea。新的无导数算法可以保持ea的优异性能。
This paper proposes a hybrid evolutionary algorithm (EA) dealing with population-based incremental learning (PBIL) and some efficient local search strategies. A simple PBIL using real codes is developed. The evolutionary direction and approximate gradient operators are integrated to the main procedure of PBIL. The method is proposed for single objective global optimization. The search performance of the developed hybrid algorithm for box-constrained optimization is compared with a number of well-established and newly developed evolutionary algorithms and meta-heuristics. It is found that, with the given optimization settings, the proposed hybrid optimizer outperforms the other EAs. The new derivative-free algorithm can maintain outstanding abilities of EAs.