The effect of mutation rate in the evolution of bidding strategies

Many have treated mutation operators as a supplement operator in genetic algorithm. Researches have shown that the mutation operator plays an important role in genetic algorithm. This paper investigates the influences of the variation of mutation 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. Genetic algorithm has shown promising result in the previous work in this particular setting. However, a fixed mutation rate (P m 0.02) is used in the algorithm 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 mutation 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 mutation rates in genetic algorithm in searching for the most effective strategies in the heuristic decision making framework are discussed in this paper.