PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Analysis of Adaptive Operator Selection Techniques on the Royal Road and Long K-Path Problems
Alvaro Fialho, Marc Schoenauer and Michele Sebag
In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO), July 2009, Montreal.


One of the choices that most affect the performance of Evolutionary Algorithms is the selection of the variation operators that are efficient to solve the problem at hand. This work presents an empirical analysis of different Adaptive Operator Selection (AOS) methods, i.e. techniques that automatically select the operator to be applied between the available ones, while searching for the solution. Four previously published operator selection rules are combined to four different credit assignment mechanisms. These 16 AOS combinations are analyzed and compared in the light of two well-known benchmark problems in EC, the Royal Road and the Long K-Path.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5122
Deposited By:Marc Schoenauer
Deposited On:24 March 2009