https://scholars.lib.ntu.edu.tw/handle/123456789/393261
標題: | Theoretical perspective of convergence complexity of evolutionary algorithms adopting optimal mixing | 作者: | TIAN-LI YU Tung, Yu-Fan |
關鍵字: | Convergence complexity; Convergence time; Number of function evaluations; Optimal mixing; Population sizing | 公開日期: | 2015 | 起(迄)頁: | 535-542 | 來源出版物: | GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference | 摘要: | The optimal mixing evolutionary algorithms (OMEAs) have recently drawn much attention for their robustness, small size of required population, and efficiency in terms of number of function evaluations (NFE). In this paper, the performances and behaviors of convergence in OMEAs are studied by investigating the mechanism of optimal mixing (OM), the variation operator in OMEAs, under two scenarios-one-layer and two-layer masks. For the case of one-layer masks, the required population size is derived from the viewpoint of initial supply, while the convergence time is derived by analyzing the progress of sub-solution growth. NFE is then asymptotically bounded with rational probability by estimating the probability of performing evaluations. For the case of two-layer masks, empirical results indicate that the required population size is proportional to both the degree of cross competition and the results from the one-layermask case. The derived models also indicate that population sizing is decided by initial supply when disjoint masks are adopted, that the high selection pressure imposed by OM makes the composition of sub-problems impact little on NFE, and that the population size requirement for two-layer masks increases with the reverse-growth probability. © 2015 ACM. |
URI: | http://www.scopus.com/inward/record.url?eid=2-s2.0-84963682502&partnerID=MN8TOARS http://scholars.lib.ntu.edu.tw/handle/123456789/393261 |
DOI: | 10.1145/2739480.2754685 | SDG/關鍵字: | Algorithms; Computational complexity; Function evaluation; Mixing; Population statistics; Probability; Convergence complexity; Convergence time; Optimal mixing; Population sizes; Population sizing; Selection pressures; Solution growth; Variation operator; Evolutionary algorithms |
顯示於: | 電機工程學系 |
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