The effects of using a greedy factor in hexapod gait learning

Various selection schemes have been described for use in genetic algorithms. This paper investigates the effects of adding greediness to the standard roulette-wheel selection. The results of this study are tested on a Cyclic Genetic Algorithm (CGA) used for learning gaits for a hexapod servo-robot. The effectiveness of CGA in learning optimal gaits with selection based on roulette-wheel selection with and without greediness is compared. The results were analyzed based on fitness of the individual gaits, convergence time of the evolution process, and the fitness of the entire population evolved. Results demonstrate that selection with too much greediness tends to prematurely converge with a sub-optimal solution, which results in poorer performance compared to the standard roulette-wheel selection. On the other hand, roulette-wheel selection with very low greediness evolves more diverse and fitter populations with individuals that result in the desired optimal gaits.