Using cultural algorithms to evolve strategies in agent-based models

Abstract

Cultural algorithms are self-adaptive models that support the collective evolution process through the employment of a population and a belief space. The cultural approach is applied to derive a generalized set of beliefs from successive populations of parameter configurations from an agent-based simulation of transactions within a durable goods market. The maintenance of this information allows for the guided evolution of the agent-based system over successive simulations. In order to more effectively evaluate parameter configurations, software engineering techniques of white and black box testing are applied. In this paper, a methodology for the use of cultural algorithms to optimize strategies in agent-based models is presented. This approach is demonstrated in an application used to model pricing strategies in the context of an agent-based model under a simulated real-world market scenario and a heterogeneous population.