Abstract:
Most estimation of distribution algorithms only make use of some high quality individuals and neglect other low quality individuals. However like high quality individuals...Show MoreMetadata
Abstract:
Most estimation of distribution algorithms only make use of some high quality individuals and neglect other low quality individuals. However like high quality individuals, these neglected low quality individuals also contain some important information that may be useful for guiding the search of estimation of distribution algorithms. This paper proposes a novel kind of estimation of distribution algorithms, where both high quality and low quality individuals in the old population are employed for reproducing new candidate individuals at the next generation. In particular, both the density PH(X) of high quality individuals and the density PL(X) of low quality individuals are estimated; then the new population G is obtained with the following steps employed: 1) a new candidate individual x is reproduced through sampling from the density PH(X); 2) to let PH(X = x) and PL(X = x) compare and the individual x will be stored into the new population G if and only if PH(X = x) ges PL(X = x); 3) the above steps repeat until M new individuals have been successfully generated where M is the population size. To demonstrate the usefulness of low quality individuals for estimation of distribution algorithms, estimation of distribution algorithms using both high quality and low quality individuals are tested on several benchmark problems and their results are compared with those obtained by estimation of distribution algorithms where only high quality individuals are used. The usefulness of low quality individuals for speeding up the search of estimation of distribution algorithms is confirmed by the experimental results.
Published in: 2009 IEEE International Conference on Fuzzy Systems
Date of Conference: 20-24 August 2009
Date Added to IEEE Xplore: 02 October 2009
ISBN Information:
Print ISSN: 1098-7584