On the relativity in the assessment of blind optimization algorithms and the problem-algorithm coevolution

Authors:
Carlos D. Toledo-Suárez;Manuel Valenzuela-Rendón;Hugo Terashima-Marín;Eduardo Uresti-Charre
Affiliations:
Instituto Tecnológico y de Estudios Superiores de Monterrey;Instituto Tecnológico y de Estudios Superiores de Monterrey;Instituto Tecnológico y de Estudios Superiores de Monterrey;Instituto Tecnológico y de Estudios Superiores de Monterrey
Venue:
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Year:
2007

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Abstract

Considering as an optimization problem the one of knowing what is hard for a blind optimization algorithm, the usefulness of absolute algorithm-independent hardness measures is called into question, establishing as a working hypothesis the relativity in the assessment of blind search. The results of the implementation of an incremental coevolutionary algorithm for coevolving populations of tunings of a simple genetic algorithm and simulated annealing, random search and 20-bit problems are presented, showing how these results are related to two popular views of hardness for genetic search: deception and rugged fitness landscapes.