title: Building blocks, cohort genetic algorithms, and hyperplane-defined functions.
abstract: Building blocks are a ubiquitous feature at all levels of human understanding, froin perception through science and innovation. Genetic algorithms are designed to exploit this prevalence. A new, more robust class of genetic algorithms, cohort genetic algorithms (cGA's), provides substantial advantages in exploring search spaces for building blocks while exploiting building blocks already found. To test these capabilities, a new, general class of test functions, the hyperplane-defined functions (hdf's), has been designed. Hdf's offer the means of tracing the origin of each advance in performance; at the same time hdf's are resistant to reverse engineering, so that algorithms cannot be designed to take advantage of the characteristics of particular examples.
authors.affiliation: The University of Michigan, Ann Arbor 48109, USA.
authors.forename: J H
authors.initials: JH
authors.lastname: Holland
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D006801:Humans; D008957:Models, Genetic