Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
A Variant of Evolution Strategies for Vector Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Evolutionary Multiobjective Optimization: Theoretical Advances and Applications (Advanced Information and Knowledge Processing)
Multiobjective Evolutionary Algorithms and Applications (Advanced Information and Knowledge Processing)
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
A model of co-evolution in multi-agent system
CEEMAS'03 Proceedings of the 3rd Central and Eastern European conference on Multi-agent systems
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
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 algorithms are a special type of evolutionary algorithms, in which the fitness of each individual depends on other individuals' fitness. Such algorithms are applicable in the case of problems for which the formulation of explicit fitness function is difficult or impossible. Co-evolutionary algorithms also maintain population diversity better than "classical" evolutionary algorithms. In this paper the agent-based version of co-operative co-evolutionary algorithm is presented and applied to multi-objective test problems. The proposed technique is also compared to two "classical" multi-objective evolutionary algorithms.