Research on a Multi-Objective Genetic Algorithm for Rational Agent Learning Model

Abstract:

As a software component which is capable of learning in an autonomous way, software agent should have the capability of learning in a dynamic environment. Genetic Algorithm has a wide perspective in the machine learning because of its unique characteristic (e.g. dynamic adaptability, self-organization, global convergence and robustness). But when applying GA to agent’s dynamic learning model, it encounters a series of problem. In this paper, a Modifided Multi-Objective Genetic Algorithm(MMOGA) will be introduced to solve these problems. Finally, an Agent’s Dynamic learning model based on a MMOGA which has the flexible dynamic learning capability, better global convergence and performance, will be introduced.

• Periodical: Applied Mechanics and Materials (Volumes 58-60)
• Pages: 1232-1239
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.58-60.1232
• Online since: June 2011
• Authors: Ming Li, Zhen Hong Xiao, Zan Fu Xie, Xiao Yun Mo
• Keywords: Agent Technology, Dynamic Learning Model, Multi-Agent System (MAS), Multi-Objective Genetic Algorithm

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