Towards a Generic Control Strategy for Evolutionary Algorithms: an Adaptive Fuzzy-Learning Approach

This paper presents a new method to generalize strategies in order to control parameters of Evolutionary Algorithms (EAs). A learning process establishes the relationship between optimal quality parameters and diversity, and simplifies control to just one variable, highly correlated with Exploration...

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Bibliographic Details
Main Authors: Maturana, J., Saubion, F.
Format: Conference Proceeding
Language:English
Subjects:
Adaptive control
Convergence
Evolutionary computation
Fuzzy logic
Genetic mutations
Optimal control
Programmable control
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Summary:This paper presents a new method to generalize strategies in order to control parameters of Evolutionary Algorithms (EAs). A learning process establishes the relationship between optimal quality parameters and diversity, and simplifies control to just one variable, highly correlated with Exploration/Exploitation Balance, in such way that strategies can be defined in more abstract terms. The acquired knowledge is expressed in a simple fashion that helps the user to understand internal mechanics of EA. The model is built after a careful example gathering and encoded in Fuzzy Logic Controllers.
ISSN:1089-778X
1941-0026
DOI:10.1109/CEC.2007.4425067