Estimation of Distribution Algorithms

Cecilia Oversdotter Alm is working on an adaptation of active interactive genetic algorithms (see here) to her work on speech synthesis and perception of emotions in expressive storytelling. She needs a version of the active interactive genetic algorithm that works on continuous domains. For that reason I coded a version of UMDAc to replace the cGA currently used for discrete domains. The Java implementation of UMDAc can be found here. In order to run it, you need to download the COLT toolkit . The code is distributed under GPL license. ...

by Xavier Llorà, Kumara Sastry, and David E. Goldberg (2006). Proceedings of the Congress on Evolutionary Computation, 1, 596—603. Also as IlliGAL TR No 2005019. Link to the PDF. Abstract This paper presents an analysis of how maximally general and accurate rules can be evolved in a Pittsburgh-style classifier system. In order to be able to perform such an analysis we introduce a simple bare-bones Pittsburgh-style classifier systems—the compact classifier system (CCS)—based on estimation of distribution algorithms. Using a common rule encoding schemes of Pittsburgh-style classifier systems, CCS mantains a dynamic set of probability vectors that compactly describe a rule set. The compact genetic algorithm is used to evolve each of the initially perturbated probability vectors. Results show how CCS is able to evolve in a compact, simple, and elegant manner rule sets composed by maximally general and accurate rules. The initial theoretical analysis and results also show that traditional encoding schemes used by Pittsburgh-style classifiers add an extra facet of diffiiculty. Such a bias plays a central role on the overall performance and scalability of CCS and other Pittsburgh-style systems using such encoding schemes. ...