Analysis of a triploid genetic algorithm over deceptive and epistatic landscapes

Abstract

This paper examines the performance of a canonical genetic algorithm (CGA) against that of the triploid genetic algorithm (TGA) introduced in [14], over a number of well known deceptive landscapes and a series of NK landscapes in order to increase our understanding of the the TGA's ability to control convergence. The TGA incorporates a mechanism to control the convergence direction instead of simply increasing the population diversity. Results indicate that the TGA appears to have the highest level of difficulty in solving problems with a disordered pattern. While these problems seem to improve the CGA's performance, it has a negative affect on the performance of the TGA. However, the results illustrate that the TGA performs better on NK-like problems (i.e. the overlapped problems) and NK problems with higher levels of epistasis.