Knowledge-based approaches to self-adaptation in cultural algorithms
Knowledge-based approaches to self-adaptation in cultural algorithms
A Rule-Based Similarity Measure
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Intelligent Optimization via Learnable Evolution Model
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Sporadic model building for efficiency enhancement of the hierarchical BOA
Genetic Programming and Evolvable Machines
Using previous models to bias structural learning in the hierarchical BOA
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Improvement of intelligent optimization by an experience feedback approach
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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Several recent works have examined the effectiveness of using knowledge models to guide search algorithms in high dimensional spaces. It seems that it may be a promising way to tackle some difficult problem. The aim of such methods is to reach good solutions using simultaneously evolutionary search and knowledge guidance. The idea proposed in this paper is to use a bayesian network in order to store and apply the knowledge model and, as a consequence, to accelerate the search process. A traditional evolutionary algorithm is modified in order to allow the reuse of the capitalized knowledge. The approach has been applied to a problem of selection of project scenarios in a multi-objective context. A preliminary version of this method was presented at EA' 07 conference [1]. An experimentation platform has been developed to validate the approach and to study different modes of knowledge injection. The obtained experimental results are presented.