Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
A model of co-evolution in multi-agent system
CEEMAS'03 Proceedings of the 3rd Central and Eastern European conference on Multi-agent systems
Multi-objective optimization using co-evolutionary multi-agent system with host-parasite mechanism
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
An Evolutionary Solution for Cooperative and Competitive Mobile Agents
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
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Co-evolutionary techniques for evolutionary algorithms help overcoming limited adaptive capabilities of evolutionary algorithms, and maintaining population diversity. In this paper the idea and formal model of agent-based realization of predator-prey co-evolutionary algorithm is presented. The effect of using such approach is not only the location of Pareto frontier but also maintaining of useful population diversity. The presented system is compared to classical multi-objective evolutionary algorithms with the use of Kursawe test problem and the problem of effective portfolio building.