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The uncertainty quandary: a study in the context of the evolutionary optimization in games and other uncertain environments

Merelo, Juan J., Liberatore, Federico ORCID:, Ares, Antonio Fernández, García, Rubén, Chelly, Zeineb, Cotta, Carlos, Rico, Nuria, Mora, Antonio M., García-Sánchez, Pablo, Tonda, Alberto, de las Cuevas, Paloma and Castillo, Pedro A. 2016. The uncertainty quandary: a study in the context of the evolutionary optimization in games and other uncertain environments. Transactions on Computational Collective Intelligence XXIV, Lecture Notes in Computer Science, vol. 9770. Springer, pp. 40-60. (10.1007/978-3-662-53525-7_3)

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In many optimization processes, the fitness or the considered measure of goodness for the candidate solutions presents uncertainty, that is, it yields different values when repeatedly measured, due to the nature of the evaluation process or the solution itself. This happens quite often in the context of computational intelligence in games, when either bots behave stochastically, or the target game possesses intrinsic random elements, but it shows up also in other problems as long as there is some random component. Thus, it is important to examine the statistical behavior of repeated measurements of performance and, more specifically, the statistical distribution that better fits them. This work analyzes four different problems related to computational intelligence in videogames, where Evolutionary Computation methods have been applied, and the evaluation of each individual is performed by playing the game, and compare them to other problem, neural network optimization, where performance is also a statistical variable. In order to find possible patterns in the statistical behavior of the variables, we track the main features of its distributions, skewness and kurtosis. Contrary to the usual assumption in this kind of problems, we prove that, in general, the values of two features imply that fitness values do not follow a normal distribution; they do present a certain common behavior that changes as evolution proceeds, getting in some cases closer to the standard distribution and in others drifting apart from it. A clear behavior in this case cannot be concluded, other than the fact that the statistical distribution that fitness variables follow is affected by selection in different directions, that parameters vary in a single generation across them, and that, in general, this kind of behavior will have to be taken into account to adequately address uncertainty in fitness in evolutionary algorithms.

Item Type: Book Section
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Springer
ISBN: 978-3-662-53524-0
ISSN: 03029743
Last Modified: 26 Oct 2022 08:25

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