Agent-Based Multi-Objective Evolutionary Algorithms with Cultural and Immunological Mechanisms

Abstract

Evolutionary algorithms are heuristic techniques for finding (sub)optimal solutions for hard global optimization problems. Evolutionary algorithms may be also applied to multimodal and multi-objective problems (for example compare (Deb, 2001)). In these cases however some special techniques must be used in order to obtain multiple high-quality solutions. Most important of these mechanisms are techniques that maintain population diversity because we are interested in finding the whole set of solutions—it would be a set of non-dominated solutions in the case of multi-objective optimization (all notions and ideas of multi-objective optimization may be found in (Deb, 2001)) and a set of individuals located within basins of attraction of different local optima in the case of multi-modal optimization problems. Agent-based evolutionary algorithms result from mixing two paradigms: evolutionary algorithms and multi-agent systems. Two approaches are possible when we try to mix these two paradigms. In the first one we can use agent-based layer of the computing system as a “manager” of the computations. In this case each agent has sub-population of individuals inside of it. Agent tries to utilize computational resources in a best way—it observes the computational environment and tries to migrate to nodes which have free computational resources. In this approach evolving individuals are processed with the use of standard evolutionary algorithm. In the second approach individuals are agents which live within the environment composed of computational nodes, compete for resources, reproduce, die, observe the environment and other agents, communicate with other agents, and can change the environment. The selection is realized in the decentralized way: there are some resources defined in the system and “worse” agents (which have “worse” solutions encoded within their genotypes) give some amount of their resources to “better” agents. These resources are needed for all activities, like reproduction, migration, etc. When an agent runs out of resources it dies and is removed from the system. The example of the second approach is