Analysis of a triploid genetic algorithm over deceptive landscapes

Authors:
Li Meng;Seamus Hill;Colm O'Riordan
Affiliations:
National University of Ireland, Galway;National University of Ireland, Galway;National University of Ireland, Galway
Venue:
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Year:
2012

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Abstract

This paper compares the performance of a canonical genetic algorithm (CGA) against that of the triploid genetic algorithm (TGA) introduced in [10], over a number of well known deceptive landscapes in order to increase our understanding of 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 the disorder-mapping seems to improve the CGA's performance, it has a negative effect on the performance of the TGA. However, the results illustrate that the TGA performs better on problems with epistasis present.