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Abstract
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| Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain. | |
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International
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Si |
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JCR
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No |
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Title
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BioData Mining |
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ISBN
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1756-0381 |
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Impact factor JCR
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0 |
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Impact info
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Volume
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Journal number
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0 |
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From page
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1 |
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To page
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12 |
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Month
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ENERO |
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Ranking
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