Authors:
Juan J. Merelo
1
;
Federico Liberatore
1
;
Antonio Fernández Ares
1
;
Rubén García
1
;
Zeineb Chelly
2
;
Carlos Cotta
3
;
Nuria Rico
1
;
Antonio M. Mora
1
and
Pablo García-Sánchez
1
Affiliations:
1
University of Granada, Spain
;
2
Institut Supérieur de Gestion, Spain
;
3
University of Málaga, Spain
Keyword(s):
Games, Evolutionary Optimization, Noise, Uncertainty, Noisy Fitness.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
Abstract:
Noise or uncertainty appear in many optimization processes when there is not a single measure of optimality
or fitness but a random variable representing it. These kind of problems have been known for a long time,
but there has been no investigation of the statistical distribution those random variables follow, assuming in
most cases that it is distributed normally and, thus, it can be modelled via an additive or multiplicative noise
on top of a non-noisy fitness. In this paper we will look at several uncertain optimization problems that
have been addressed by means of Evolutionary Algorithms and prove that there is no single statistical model
the evaluations of the fitness functions follow, being different not only from one problem to the next, but in
different phases of the optimization in a single problem.