Towards a Genetic Algorithm for Function Optimization (original) (raw)

Saint Mary's University, Canada, Halifax

This article analyses a version of genetic algorithm (GA, Holland 1975) designed for function opt imization, which is simple and reliable for most applications. The novelty in cur rent approach is random provision of parameters, created by the GA. Chromosome portions which do not t ranslate into fitness are given functio n to d iversify contr ol parameters for t he GA, pr oviding random parameter setting along the way, and doing away with fine-tuning of probabilities of crossover and mutation. We test our algorithm on Royal Road functions to examine the difference between our version ( GAW) and t he simple GA (SGA) in the speed of discovering schema and creating building blocks. We also look at the usefulness of other standard improvements, such as non-coding segments, elitist selection and multiple crossover. 902 -42 056 07 ( tel) 902-4205129 (fax) * ABS Americas 16855 Northchase Drive Houston, TX 77060 dsverko@eagle.org 1 Probability of crossover, pr obability of mutation and popul ation siz e. 2 Fitness dependency may cause a problem with systems in which string fitness depends on the state of the population (Dawid, 1997).

Sign up for access to the world's latest research.

checkGet notified about relevant papers

checkSave papers to use in your research

checkJoin the discussion with peers

checkTrack your impact