Foundations of genetic algorithms
Foundations of genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Serial and Parallel Genetic Algorithms as Function Optimizers
Proceedings of the 5th International Conference on Genetic Algorithms
Epistasis in Genetic Algorithms: An Experimental Design Perspective
Proceedings of the 6th International Conference on Genetic Algorithms
Multi-chromosomal genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
GARS: an improved genetic algorithm with reserve selection for global optimization
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A tunable model for multi-objective, epistatic, rugged, and neutral fitness landscapes
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Solving discrete deceptive problems with EMMRS
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Analysis of epistasis correlation on NK landscapes with nearest-neighbor interactions
Proceedings of the 13th annual conference on Genetic and evolutionary computation
An analysis of multi-chromosome GAs in deceptive problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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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.