A fundamental aspect of many evolutionary approaches to synthesis of complex systems is the need to compose atomic elements into useful higher-level building blocks. However, the ability of genetic algorithms to promote useful building blocks is based critically on genetic linkage - the assumption that functionally related alleles are also arranged compactly on the genome. In many practical problems, linkage is not known a priori or may change dynamically. Here we propose that a problem's Hessian matrix reveals this linkage, and that an eigenstructure analysis of the Hessian provides a transformation of the problem to & space where first-order genetic linkage is optimal. Genetic algorithms that dynamically transforms the problem space can operate much more efficiently. We demonstrate the proposed approach on a real-valued adaptation of Kaufmann's NK landscapes and discuss methods for extending it to higher-order linkage. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Wyatt, D., & Lipson, H. (2003). Finding building blocks through eigenstructure adaptation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2724, 1518–1529. https://doi.org/10.1007/3-540-45110-2_23
Mendeley helps you to discover research relevant for your work.