New requirements for off-line parameter calibration algorithms

The process of designing an evolutionary algorithm requires the definition of an adequate representation, a set of components as operators, parameters and parameters values. In practice, some operators can not be helping the evolutionary algorithm to perform his work, thus we require to be able to detect these situations. In this paper we are interested on analyzing the capabilities of off-line calibration techniques, originally designed to tune parameter values, to recognize situations more related to the design process of an evolutionary algorithm. We experimentally analyze the results of the off-line calibration techniques on some specific situations. For that purpose we use some specially designed operators for an evolutionary algorithm which solves the traveling salesman problem.

[1]  David E. Goldberg,et al.  A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..

[2]  Martijn C. Schut,et al.  New Ways to Calibrate Evolutionary Algorithms , 2008, Advances in Metaheuristics for Hard Optimization.

[3]  K. G. Srinivasa,et al.  A self-adaptive migration model genetic algorithm for data mining applications , 2007, Inf. Sci..

[4]  Manuel Laguna,et al.  Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search , 2006, Oper. Res..

[5]  Holger H. Hoos,et al.  A replica exchange Monte Carlo algorithm for protein folding in the HP model , 2007, BMC Bioinformatics.

[6]  Zbigniew Michalewicz,et al.  Genetic algorithms + data structures = evolution programs (3rd ed.) , 1996 .

[7]  Alan J. Hu,et al.  Boosting Verification by Automatic Tuning of Decision Procedures , 2007 .

[8]  Thomas Stützle,et al.  Applications of Racing Algorithms: An Industrial Perspective , 2005, Artificial Evolution.

[9]  Zbigniew Michalewicz,et al.  Parameter Setting in Evolutionary Algorithms , 2007, Studies in Computational Intelligence.

[10]  Thomas Stützle,et al.  Automatic Algorithm Configuration Based on Local Search , 2007, AAAI.

[11]  Ritu Gupta,et al.  Statistical exploratory analysis of genetic algorithms , 2004, IEEE Transactions on Evolutionary Computation.

[12]  Thomas Bartz-Beielstein,et al.  Sequential parameter optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[13]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[14]  Cláudio F. Lima,et al.  Adaptive Population Sizing Schemes in Genetic Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

[15]  A. E. Eiben,et al.  Efficient relevance estimation and value calibration of evolutionary algorithm parameters , 2007, 2007 IEEE Congress on Evolutionary Computation.

[16]  Xavier Bonnaire,et al.  Inheriting Parents Operators: A New Dynamic Strategy for Improving Evolutionary Algorithms , 2002, ISMIS.

[17]  María Cristina Riff,et al.  Calibrating strategies for evolutionary algorithms , 2007, 2007 IEEE Congress on Evolutionary Computation.

[18]  Thomas Stützle,et al.  A Racing Algorithm for Configuring Metaheuristics , 2002, GECCO.

[19]  A. E. Eiben,et al.  Parameter calibration using meta-algorithms , 2007, 2007 IEEE Congress on Evolutionary Computation.

[20]  Oliver Kramer.  Self-Adaptive Heuristics for Evolutionary Computation , 2008, Studies in Computational Intelligence.