کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
411146 679182 2009 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A dynamic chain-like agent genetic algorithm for global numerical optimization and feature selection
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
A dynamic chain-like agent genetic algorithm for global numerical optimization and feature selection
چکیده انگلیسی

In this paper, one novel genetic algorithm dynamic chain-like agent genetic algorithm (CAGA) is proposed for solving global numerical optimization problem and feature selection problem. The CAGA combines the chain-like agent structure with dynamic neighboring genetic operators to get higher optimization capability. An agent in chain-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and can use knowledge to increase energies. Global numerical optimization problem and feature selection problem are the most important problems for evolutionary algorithm, especially for genetic algorithm. Hence, the experiments of global numerical optimization and feature selection are necessary to verify the performance of genetic algorithms. Corresponding experiments have been done and show that CAGA is suitable for real coding and binary coding optimization problems, and has more precise and more stable optimization results.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 72, Issues 4–6, January 2009, Pages 1214–1228
نویسندگان
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