Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On the stationary distribution of GAs with fixed crossover probability
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A building-block royal road where crossover is provably essential
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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Many researchers have shown that the crossover operator is essential for genetic algorithm. Crossover in genetic algorithm works to combine short low-order schemata into high-fitness strings. This paper investigates the influences of the variation of crossover rate in genetic algorithms when applied to bidding strategies in online auctions. The proposed bidding strategy is polynomial in nature in which it will suggest the price to bid at a given time depending on some constraints. The best strategy is discovered by evolving the strategies using the traditional genetic algorithm. In this particular setting, GA has shown promising results in exploring the solution in large space and with little priori information. However, the crossover rate was fixed at 0.6 as suggested by the literature. It cannot be ascertained if this is the best value to use. Hence, the objective of this work is to investigate the effect of varying the crossover rate by observing the performance of the bidding strategy in the online auctions based on the average fitness, success rate and the average payoff. An empirical evaluation on the relative performance of the various crossover rates in genetic algorithm in searching for the most effective strategies in the heuristic decision making framework are discussed in this paper.