Economic Modeling Using Evolutionary Algorithms: The Influence of Mutation on the Premature Convergence Effect

Abstract

This work is concerned with the possible impact binary encoding of strategies may have on the performance of genetic algorithms popular in agent-based computational economic research. In their recent work, Waltman et al. (J Evol Econ 21(5): 737---756, 2011) consider binary encoding and its possible contribution to a phenomenon referred to as premature convergence; the observation that different individual runs of the genetic algorithm can lead to very different results. While Alkemade et al. (Comput Econ 28(4): 355---370, 2006), (Comput Intell 23(2): 162---175, 2007), (Comput Econ 33(1): 99---101, 2009) argue that premature convergence is caused by insufficient population size, Waltman et al. argue that this phenomenon depends crucially on strategies being encoded in binary form. This conclusion is based on their illustration that premature convergence can be avoided even in simulations with small populations so long as real, rather than binary, encoding of strategies is utilized. Utilizing their methodology, we return to the consideration of the cause of premature convergence. After robustness checks with respect to the length of the binary string used for encoding, the fitness function, and the form of mutation, it is concluded that an alternative specification of mutation may also alleviate the occurrence of premature convergence. It is argued that this alternative form of mutation may be more appropriate in a wider range of problems where real encoding of strategies may not prove sufficient.