Optimization of cost functions using evolutionary algorithms with local learning and local search

被引:29
作者
Guimaraes, Frederico G. [1 ]
Campelo, Felipe
Igarashi, Hajime
Lowther, David A.
Ramirez, Jaime A.
机构
[1] Univ Fed Minas Gerais, Dept Elect Engn, BR-31270010 Belo Horizonte, MG, Brazil
[2] Hokkaido Univ, Grad Sch informat Sci & Technol, Lab Hybrid Syst, Sapporo, Hokkaido 0600814, Japan
[3] McGill Univ, Dept Elect & Comp Engn, Montreal, PQ H3A 2K6, Canada
关键词
evolutionary algorithms; hybrid methods; memetic algorithms (MAs);
D O I
10.1109/TMAG.2007.892486
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Evolutionary algorithms can benefit from their association with local search operators, giving rise to hybrid or memetic algorithms. The cost of the local search may be prohibitive, particularly when dealing with computationally expensive functions. We propose the use of local approximations in the local search phase of memetic algorithms for optimization of cost functions. These local approximations are generated using only information already collected by the algorithm during the evolutionary process, requiring no additional evaluations. The local search improves some individuals of the population, hence speeding up the overall optimization process. We investigate the design of a loudspeaker magnet with seven variables. The results show the improvement achieved by the proposed combination of local learning and search within evolutionary algorithms.
引用
收藏
页码:1641 / 1644
页数:4
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