Towards a Genetic Algorithm for Function Optimization

                Sonja Novkovic and Davor Sverko

Abstract:

This article analyses a version of genetic algorithm (GA, Holland 
1975) designed for function optimization, which is simple and reliable
for most applications. The novelty in current approach is random 
provision of parameters, created by the GA. Chromosome portions which
do not translate into fitness are given function to diversify control 
parameters for the GA, providing 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 the 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.