Robert G. Reynolds

Abstract - Various biologically inspired approaches to problem solving using a social metaphor have been proposed. For example, both Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) have been employed to solve problems in optimization and design. Both approaches employ simple social interactions between agents to produce emergent social structures that are used to solve a given problem. In this paper we investigate the emergence and power of more complex social systems based upon principles of cultural evolution. Cultural Algorithms employ a basic set of knowledge sources, each related to knowledge observed in various social species. These knowledge sources are then combined to direct the decisions of the individual agents in solving optimization problems. Here we develop an algorithm based upon an analogy to the marginal value theorem in foraging theory to guide the integration of these different knowledge sources to direct the agent population. Various phases of problem solving emerge from the combined use of these knowledge sources and these phases result in the emergence of individual roles within the population in terms of leaders and followers. These roles result in organized swarming in the population level and knowledge swarms in the social belief space. Application to real-valued function optimization in engineering design is used to illustrate the principles