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Authors: J. J. Merelo 1 ; Pedro A. Castillo 1 ; Antonio Mora 1 ; Antonio Fernández-Ares 1 ; Anna I. Esparcia-Alcázar 2 ; Carlos Cotta 3 and Nuria Rico 4

Affiliations: 1 University of Granada, Spain ; 2 S2Grupo, Spain ; 3 Universidad de Málaga, Spain ; 4 Universidad de Granada, Spain

ISBN: 978-989-758-052-9

Keyword(s): Evolutionary Algorithms, Noisy Optimization Problems, Games, Strategy Games.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Computational Intelligence ; Enterprise Information Systems ; Evolutionary Computing ; Game Theory Applications ; Genetic Algorithms ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Representation Techniques ; Soft Computing

Abstract: In most computer games as in life, the outcome of a match is uncertain due to several reasons: the characters or assets appear in different initial positions or the response of the player, even if programmed, is not deterministic; different matches will yield different scores. That is a problem when optimizing a game-playing engine: its fitness will be noisy, and if we use an evolutionary algorithm it will have to deal with it. This is not straightforward since there is an inherent uncertainty in the true value of the fitness of an individual, or rather whether one chromosome is better than another, thus making it preferable for selection. Several methods based on implicit or explicit average or changes in the selection of individuals for the next generation have been proposed in the past, but they involve a substantial redesign of the algorithm and the software used to solve the problem. In this paper we propose new methods based on incremental computation (memory-based) or fitness a verage or, additionally, using statistical tests to impose a partial order on the population; this partial order is considered to assign a fitness value to every individual which can be used straightforwardly in any selection function. Tests using several hard combinatorial optimization problems show that, despite an increased computation time with respect to the other methods, both memory-based methods have a higher success rate than implicit averaging methods that do not use memory; however, there is not a clear advantage in success rate or algorithmic terms of one method over the other (More)

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Paper citation in several formats:
Merelo, J.; Castillo, P.; Mora, A.; Fernández-Ares, A.; Esparcia-Alcázar, A.; Cotta, C. and Rico, N. (2014). Studying and Tackling Noisy Fitness in Evolutionary Design of Game Characters.In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014) ISBN 978-989-758-052-9, pages 76-85. DOI: 10.5220/0005085700760085

@conference{ecta14,
author={J. J. Merelo. and Pedro A. Castillo. and Antonio Mora. and Antonio Fernández{-}Ares. and Anna I. Esparcia{-}Alcázar. and Carlos Cotta. and Nuria Rico.},
title={Studying and Tackling Noisy Fitness in Evolutionary Design of Game Characters},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)},
year={2014},
pages={76-85},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005085700760085},
isbn={978-989-758-052-9},
}

TY - CONF

JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)
TI - Studying and Tackling Noisy Fitness in Evolutionary Design of Game Characters
SN - 978-989-758-052-9
AU - Merelo, J.
AU - Castillo, P.
AU - Mora, A.
AU - Fernández-Ares, A.
AU - Esparcia-Alcázar, A.
AU - Cotta, C.
AU - Rico, N.
PY - 2014
SP - 76
EP - 85
DO - 10.5220/0005085700760085

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