標題: Enabling the Extended Compact Genetic Algorithm for Real-Parameter Optimization by Using Adaptive Discretization
作者: Chen, Ying-ping
Chen, Chao-Hong
資訊工程學系
Department of Computer Science
關鍵字: Estimation of distribution algorithm;EDA;ECGA;split-on-demand;SoD;real-parameter optimization;economic dispatch;valve point effect
公開日期: 1-六月-2010
摘要: An adaptive discretization method, called split-on-demand (SoD), enables estimation of distribution algorithms (EDAs) for discrete variables to solve continuous optimization problems. SoD randomly splits a continuous interval if the number of search points within the interval exceeds a threshold, which is decreased at every iteration. After the split operation, the nonempty intervals are assigned integer codes, and the search points are discretized accordingly. As an example of using SoD with EDAs, the integration of SoD and the extended compact genetic algorithm (ECGA) is presented and numerically examined. In this integration, we adopt a local search mechanism as an optional component of our back end optimization engine. As a result, the proposed framework can be considered as a memetic algorithm, and SoD can potentially be applied to other memetic algorithms. The numerical experiments consist of two parts: (1) a set of benchmark functions on which ECGA with SoD and ECGA with two well-known discretization methods: the fixed-height histogram (FHH) and the fixed-width histogram (FWH) are compared; (2) a real-world application, the economic dispatch problem, on which ECGA with SoD is compared to other methods. The experimental results indicate that SoD is a better discretization method to work with ECGA. Moreover, ECGA with SoD works quite well on the economic dispatch problem and delivers solutions better than the best known results obtained by other methods in existence.
URI: http://dx.doi.org/10.1162/evco.2010.18.2.18202
http://hdl.handle.net/11536/5353
ISSN: 1063-6560
DOI: 10.1162/evco.2010.18.2.18202
期刊: EVOLUTIONARY COMPUTATION
Volume: 18
Issue: 2
起始頁: 199
結束頁: 228
顯示於類別:期刊論文


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