Abstract
Estimation of distribution algorithms (EDAs) are a class of evolutionary algorithms that use probabilistic model of promising solutions found so far to obtain new candidate solutions of optimized problems. One of the charming characteristics of this evolutionary paradigm is that the joint probability density can explicitly represent correlation among variables [1,16]. It is verified by several researchers [2] that multivariate-dependency EDAs have the potential ability to optimize hard problems with strong nonlinearity. However, it is also noted that how to efficiently learn the complex probabilistic model is a bottleneck problem. Therefore, obtaining a good balance between the complication of probabilistic models and the efficiency of learning method is a key factor for designing new EDAs.
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Ding, N., Zhou, S. (2008). Linkages Detection in Histogram-Based Estimation of Distribution Algorithm. In: Chen, Yp., Lim, MH. (eds) Linkage in Evolutionary Computation. Studies in Computational Intelligence, vol 157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85068-7_2
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DOI: https://doi.org/10.1007/978-3-540-85068-7_2
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