Graph Based Evolutionary Algorithms

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Author(s): Steven M. Corns (Iowa State University, USA), Daniel A. Ashlock (University of Guelph, Canada)and Kenneth Mark Bryden (Iowa State University, USA)
Copyright: 2009
Pages: 20
Source title: Advancing Artificial Intelligence through Biological Process Applications
Source Author(s)/Editor(s): Ana B. Porto Pazos (Coruna University, Spain), Alejandro Pazos Sierra (Coruna University, Spain)and Washington Buño Buceta (Cajal Institute, Spanish Council for Scientific Research, Spain)
DOI: 10.4018/978-1-59904-996-0.ch017

Keywords: Bioinformatics / Biologically-Inspired Computing / Medical Information Science Reference / Medicine & Healthcare

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Abstract

This chapter presents Graph Based Evolutionary Algorithms. Graph Based Evolutionary Algorithms are a generic enhancement and diversity management technique for evolutionary algorithms. These geographically inspired algorithms are different from other methods of diversity control in that they not only control the rate of diversity loss at low runtime cost but also allow for a means to classify evolutionary computation problems. This classification system enables users to select an algorithm a priori that finds a satisfactory solution to their optimization problem in a relatively small number of fitness evaluations. In addition, using the information gathered by evaluating several problems on a collection of graphs, it becomes possible to design test suites of problems which effectively compare a new algorithm or technique to existing methods.

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