Genetic Programming Method of Evolving the Robotic Soccer Player Strategies with Ant Intelligence | Zendy

R. Ramani | Zendy; R. Subramanian | Zendy; P. Viswanath | Zendy

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Genetic Programming Method of Evolving the Robotic Soccer Player Strategies with Ant Intelligence

Author(s) -

R. Ramani,

R. Subramanian,

P. Viswanath

Publication year - 2009

Publication title -

international journal of advanced robotic systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.394

H-Index - 46

eISSN - 1729-8814

pISSN - 1729-8806

DOI - 10.5772/6790

Subject(s) - computer science , genetic programming , java , artificial intelligence , evolutionary computation , interactive evolutionary computation , swarm intelligence , code (set theory) , machine learning , evolutionary programming , human–computer interaction , programming language , particle swarm optimization , set (abstract data type)

This paper presents the evolved soccer player strategies with ant-intelligence through genetic programming. To evolve the code for players we used the Evolutionary Computation tool (ECJ simulator-Evolutionary Compuation in Java). We tested the evolved player strategies with already existing teams in soccerbots of teambots. This paper presents brief information regarding learning methods and ant behaviors. Experimental results depicts the performance of the evolved player strategies

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