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Game Theory-Inspired Evolutionary Algorithm for Global Optimization

Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Jixie Building 405 of West Campus, Huaxi District, Guiyang 550025, China
Algorithms 2017, 10(4), 111;
Received: 4 August 2017 / Revised: 20 September 2017 / Accepted: 25 September 2017 / Published: 30 September 2017
PDF [599 KB, uploaded 30 September 2017]


Many approaches that model specific intelligent behaviors perform excellently in solving complex optimization problems. Game theory is widely recognized as an important tool in many fields. This paper introduces a game theory-inspired evolutionary algorithm for global optimization (GameEA). A formulation to estimate payoff expectations is provided, which is a mechanism to make a player become a rational decision-maker. GameEA has one population (i.e., set of players) and generates new offspring only through an imitation operator and a belief-learning operator. An imitation operator adopts learning strategies and actions from other players to improve its competitiveness and applies these strategies to future games where one player updates its chromosome by strategically copying segments of gene sequences from a competitor. Belief learning refers to models in which a player adjusts his/her strategies, behavior or chromosomes by analyzing the current history information to improve solution quality. Experimental results on various classes of problems show that GameEA outperforms the other four algorithms on stability, robustness, and accuracy. View Full-Text
Keywords: game theory; game evolutionary algorithms (GameEA); genetic algorithm; imitation; adaptive learning; optimization problems game theory; game evolutionary algorithms (GameEA); genetic algorithm; imitation; adaptive learning; optimization problems

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Yang, G. Game Theory-Inspired Evolutionary Algorithm for Global Optimization. Algorithms 2017, 10, 111.

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