Using Datamining Techniques to Help Metaheuristics: A Short Survey

International audienceHybridizing metaheuristic approaches becomes a common way to improve the efficiency of optimization methods. Many hybridizations deal with the combination of several optimization methods. In this paper we are interested in another type of hybridization, where datamining approaches are combined within an optimization process. Hence, we propose to study the interest of combining metaheuristics and datamining through a short survey that enumerates the different opportunities of such combinations based on literature examples

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In this article, we model a linkage disequilibrium study (genomic study) as an optimization problem where a given objective function has to be optimized. The objective of the study is to discover haplotypes (associations of genetic markers) candidate to explain multi-factorial diseases such as diabetes or obesity. To determine what kind of algorithm will be able to solve this problem, we first study the specificities and the structure of the problem. Results of this study show that exact algorithms are not adapted to this specific problem and lead us to the development of a parallel dedicated adaptive multipopulation genetic algorithm that is able to find several haplotypes of different sizes. After describing the genomic problem, we present the dedicated genetic algorithm, its specificities, such as the use of several populations and its advanced mechanisms such as the adaptive choice of operators, random immigrants, and its parallel implementation. Results on a real dataset are given. 1