Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions

Building blocks are a ubiquitous feature at all levels of human understanding, from 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. Keywords Building blocks, chromosome-like strings, crossover, fitness, genetic algorithms, schema, search spaces, robustness, selection, test functions.

• Type: Journal
• Year: 2000
• Where: EC
• Authors: John H. Holland
• Keywords: Cohort Genetic Algorithms, EC 2000, ECommerce, Genetic Algorithms, Search Spaces