Evaluation of Evolution Strategy, Genetic Algorithm and their Hybrid on Evolving Simulated Car Racing Controllers
Researchers have been applying tional intelligence (AI/CI) methods to computer games. In this research field, further researchesare required to compare AI/CI
methods with respect to each game application. In th
our experimental result on the comparison of three evolutionary algorithms – evolution strategy, genetic algorithm, and their hybrid
applied to evolving controller agents for the CIG 2007 Simulated Car Racing competition. Our experimental result shows that, premature
convergence of solutions was observed in the case of ES, and GA outperformed ES in the last half of generations. Besides, a hybrid
which uses GA first and ES next evolved the best solution among the whole solutions being generated. This result shows the ability of GA in
globally searching promising areas in the early stage and the ability of ES in locally searching the focused area (fine-tuning solutions).
[1] H.-P.Schwefel, Evolution and Optimum Seeking. New York: Wiley & Sons, 1995.
[2] D. E.Goldberg, Genetic Algorithms in Search Optimization and Machine
Learning. Addison Wesley, 1989.
[3] X. Yao, "A review of evolutionary artificial neural
networks,"International Journal of Intelligent Systems, vol. 4, pp.539-567, 1993.
[4] K.O. Stanley and R. Miikkulainen,"Evolving neural networks through
augmenting topologies, "EvolutionaryComputation, vol.10, no.2, pp.99-127, 2002.
[5] S. Lucas, and J.Togelius, "Point-to-point car racing: an initial study of
evolution versus temporal difference learning,"Proc. of IEEE Conference
on Computational Intelligence and Games (CIG) 2007, pp.260-267,
2007.http://cswww.essex.ac.uk/cig/2007/papers/2071.pdf
[6] L.J.Eshelman, "Real-coded genetic algorithms and
interval-ichemata, "Foundations of Genetic Algorithms 2, pp.187-202,
1993.
[7] Y.S. Ong, M.H.Lim, N. Zhu and K.W.Wong, "Classification of adaptive
memetic algorithms: acomparative study,"IEEE Transactions on Systems
Man and Cybernetics-Part B, vol.36, no.1, pp.141-152, 2006.
[8] J.E.Smith, "Coevolving memetic algorithms: areview and progress
report,"IEEE Transactions on Systems Man and Cybernetics -Part B,
vol.37, no.1, pp.6-17, 2007.
[9] F. Neri, C. Cotta, and P. Moscato (eds), Handbook of Memetic
Algorithms. Springer, 2011.
[1] H.-P.Schwefel, Evolution and Optimum Seeking. New York: Wiley & Sons, 1995.
[2] D. E.Goldberg, Genetic Algorithms in Search Optimization and Machine
Learning. Addison Wesley, 1989.
[3] X. Yao, "A review of evolutionary artificial neural
networks,"International Journal of Intelligent Systems, vol. 4, pp.539-567, 1993.
[4] K.O. Stanley and R. Miikkulainen,"Evolving neural networks through
augmenting topologies, "EvolutionaryComputation, vol.10, no.2, pp.99-127, 2002.
[5] S. Lucas, and J.Togelius, "Point-to-point car racing: an initial study of
evolution versus temporal difference learning,"Proc. of IEEE Conference
on Computational Intelligence and Games (CIG) 2007, pp.260-267,
2007.http://cswww.essex.ac.uk/cig/2007/papers/2071.pdf
[6] L.J.Eshelman, "Real-coded genetic algorithms and
interval-ichemata, "Foundations of Genetic Algorithms 2, pp.187-202,
1993.
[7] Y.S. Ong, M.H.Lim, N. Zhu and K.W.Wong, "Classification of adaptive
memetic algorithms: acomparative study,"IEEE Transactions on Systems
Man and Cybernetics-Part B, vol.36, no.1, pp.141-152, 2006.
[8] J.E.Smith, "Coevolving memetic algorithms: areview and progress
report,"IEEE Transactions on Systems Man and Cybernetics -Part B,
vol.37, no.1, pp.6-17, 2007.
[9] F. Neri, C. Cotta, and P. Moscato (eds), Handbook of Memetic
Algorithms. Springer, 2011.
@article{"International Journal of Information, Control and Computer Sciences:56137", author = "Hidehiko Okada and Jumpei Tokida", title = "Evaluation of Evolution Strategy, Genetic Algorithm and their Hybrid on Evolving Simulated Car Racing Controllers", abstract = "Researchers have been applying tional intelligence (AI/CI) methods to computer games. In this research field, further researchesare required to compare AI/CI
methods with respect to each game application. In th
our experimental result on the comparison of three evolutionary algorithms – evolution strategy, genetic algorithm, and their hybrid
applied to evolving controller agents for the CIG 2007 Simulated Car Racing competition. Our experimental result shows that, premature
convergence of solutions was observed in the case of ES, and GA outperformed ES in the last half of generations. Besides, a hybrid
which uses GA first and ES next evolved the best solution among the whole solutions being generated. This result shows the ability of GA in
globally searching promising areas in the early stage and the ability of ES in locally searching the focused area (fine-tuning solutions).", keywords = "Evolutionary algorithm, autonomous agent, neuroevolutions, simulated car racing.", volume = "6", number = "5", pages = "622-4", }