An agent-based collaborative evolutionary model for multimodal optimization.
ABSTRACT A novel approach to multimodal optimization called Roaming Agent-Based Collaborative Evolutionary Model (RACE) combining several evolutionary techniques with agent-based modeling is proposed. RACE model aims to detect multiple global and local optima by training a multi-agent system to employ various evolutionary techniques suitable for a specified multimodal optimization problem. Agents can exchange information during the search process enabling a cooperative search of optima between several populations evolving independently. Redundant search by multiple agents is avoided by having them communicate and negotiate about the space region searched. An agent can request and receive from another agent valuable information and genetic material for a better search of a certain region in the environment. Performance of the proposed agent-based collaborative evolutionary model is compared by means of numerical experiments with rival evolutionary techniques.
Conference Proceeding: Multinational evolutionary algorithms[show abstract] [hide abstract]
ABSTRACT: Since practical problems often are very complex with a large number of objectives, it can be difficult or impossible to create an objective function expressing all the criteria of good solutions. Sometimes a simpler function can be used where local optimas could be both valid and interesting. Because evolutionary algorithms are population based, they have the best potential for finding more of the best solutions among the possible solutions. However, standard EAs often converge to one solution and leave therefore only this single option for a final human selection. So far, at least two methods, sharing and tagging, have been proposed to solve the problem. The paper presents a new method for finding more quality solutions, not only global optimas but local as well. The method tries to adapt its search strategy to the problem by taking the topology of the fitness landscape into account. The idea is to use the topology to group the individuals into sub-populations, each covering a part of the fitness landscapeEvolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on; 02/1999
Conference Proceeding: A new evolutionary model for detecting multiple optima.[show abstract] [hide abstract]
ABSTRACT: Multimodal optimization problems consist in detecting all global and local optima of a problem. A new evolutionary approach to multimodal optimization called Roaming tech- nique (RO) is presented. Roaming uses two original concepts in order to detect multiple optima: a stability measure for subpopulations and an external population called archive to store detected optima. Individuals in the archive are refined by evolving them independently. Performance of Roaming is compared by means of numerical experiments with two other evolutionary techniques.Genetic and Evolutionary Computation Conference, GECCO 2007, Proceedings, London, England, UK, July 7-11, 2007; 01/2007
- University of Michigan, 01/1975, Degree: PhD