.png){kind=icon}
返回
Memetic Viability Evolution for Constrained Optimization
.png){kind=icon}
OA
AI
DOI：10.1109/TEVC.2015.2428292.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The performance of evolutionary algorithms can be heavily undermined when constraints limit the feasible areas of the search space. For instance, while covariance matrix adaptation evolution strategy (CMA-ES) is one of the most efficient algorithms for unconstrained optimization problems, it cannot be readily applied to constrained ones. Here, we used concepts from memetic computing, i.e., the harmonious combination of multiple units of algorithmic information, and viability evolution, an alternative abstraction of artificial evolution, to devise a novel approach for solving optimization problems with inequality constraints. Viability evolution emphasizes the elimination of solutions that do not satisfy viability criteria, which are defined as boundaries on objectives and constraints. These boundaries are adapted during the search to drive a population of local search units, based on CMAES, toward feasible regions. These units can be recombined by means of differential evolution operators. Of crucial importance for the performance of our method, an adaptive scheduler toggles between exploitation and exploration by selecting to advance one of the local search units and/or recombine them. The proposed algorithm can outperform several state-of-the-art methods on a diverse set of benchmark and engineering problems, both for quality of solutions and computational resources needed.
Keyword:
Constrained optimization
covariance matrix adaptation
differential evolution (DE)
memetic computing (MC)
viability evolution
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
12
论文数:
1.9K
被引数:
2.4W
机构
引用论文
分享收藏
AMA: a new approach for solving constrained real-valued optimization problemsAMA: 一种求解约束实值优化问题的新方法.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏
查看更多内容