Title :
The effects of using a greedy factor in hexapod gait learning
Author :
Parker, Gary B. ; Tarimo, William T.
Author_Institution :
Dept. of Comput. Sci., Connecticut Coll., New London, CT, USA
Abstract :
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.
Keywords :
convergence; genetic algorithms; greedy algorithms; learning (artificial intelligence); legged locomotion; servomechanisms; wheels; CGA; convergence time; cyclic genetic algorithm; evolution process; greedy factor; hexapod gait learning; hexapod servorobot; standard roulette-wheel selection; suboptimal solution; Biological cells; Genetic algorithms; Leg; Legged locomotion; Servomotors; Wheels; Cyclic Control; Cyclic Genetic Algorithm; Evolutionary Robotics; Gait; Genetic Algorithm; Greedy Selection; Hexapod; Learning Control;
Conference_Titel :
Evolutionary Computation (CEC), 2011 IEEE Congress on
Conference_Location :
New Orleans, LA
Print_ISBN :
978-1-4244-7834-7
DOI :
10.1109/CEC.2011.5949794
