Incorporation family competition into Gaussian and Cauchy mutations to training neural networks using an evolutionary algorithm

Jinn Moon Yang, Jorng Tzong Horng, Cheng Yen Kao

研究成果: 會議貢獻類型會議論文同行評審

摘要

The paper presents an evolutionary technique to train neural networks in tasks requiring learning behavior. Based on family competition principles and adaptive rules, the proposed approach integrates decreasing-based mutations and self-adaptive mutations. Different mutations act global and local strategies separately to balance the trade-off between solution quality and convergence speed. The algorithm proposed herein is applied to two different task domains: Boolean functions and artificial ant problem. Experimental results indicate that in all tested problems, the proposed algorithm performs better than other canonical evolutionary algorithms, such as genetic algorithms, evolution strategies, and evolutionary programming. Moreover, essential components such as mutation operators and adaptive rules in the proposed algorithm are thoroughly analyzed.

原文???core.languages.en_GB???
頁面1994-2001
頁數8
DOIs
出版狀態已出版 - 1999
事件1999 Congress on Evolutionary Computation, CEC 1999 - Washington, DC, United States
持續時間: 6 7月 19999 7月 1999

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???event.eventtypes.event.conference???1999 Congress on Evolutionary Computation, CEC 1999
國家/地區United States
城市Washington, DC
期間6/07/999/07/99

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