Quarterly (spring, summer, fall, winter)
176 pp. per issue
7 x 10
2014 Impact factor:

Evolutionary Computation

Winter 2000, Vol. 8, No. 4, Pages 373-391
(doi: 10.1162/106365600568220)
© 2000 Massachusetts Institute of Technology
Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions
Article PDF (213.24 KB)

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.