Abstract
Genetic Algorithms are a class of metaheuristics with applications in several fields including biology, engineering and even arts. However, simple Genetic Algorithms may suffer from exponential scalability on hard problems. Estimation of Distribution Algorithms, a special class of Genetic Algorithms, can build complex models of the iterations among variables in the problem, solving several intractable problems in tractable polynomial time. However, the model building process can be computationally expensive and efficiency enhancements are oftentimes necessary to make tractable problems practical. This paper presents a new model building approach, called ClusterMI, inspired both by the Extended Compact Genetic Algorithm and the Dependency Structure Matrix Genetic Algorithm. The new approach has a more efficient model building process, resulting in speed ups of 10 times for moderate size problems and potentially hundreds of times for large problems. Moreover, the new approach may be easily extended to perform incremental evolution, eliminating the burden of representing the population explicitly.
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Duque, T.S.P.C., Goldberg, D.E. (2010). ClusterMI: Building Probabilistic Models Using Hierarchical Clustering and Mutual Information. In: Chen, Yp. (eds) Exploitation of Linkage Learning in Evolutionary Algorithms. Evolutionary Learning and Optimization, vol 3. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12834-9_6
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DOI: https://doi.org/10.1007/978-3-642-12834-9_6
Publisher Name: Springer, Berlin, Heidelberg
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