Abstract
This paper presents MONet (Multi-Objective coevolutive NETwork), a cooperative coevolutionary model for evolving artificial neural networks that introduces concepts taken from multi-objective optimization. This model is based on the idea of coevolving subnetworks that must cooperate to form a solution for a specific problem, instead of evolving complete networks. The fitness of each member of the subpopulations of subnetworks is evaluated using an evolutionary multi-objective optimization algorithm. This idea has not been used before in the area of evolutionary artificial neural networks. The use of a multiobjective evolutionary algorithm allows the definition of as many objectives as could be interesting for our problem and the optimization of these objectives in a natural way.
This work was supported in part by the Project ALI98-0676-CO2-02 of the Spanish Comisión Interministerial de Ciencia y Tecnología
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García-Pedrajas, N., Sanz-Tapia, E., Ortiz-Boyer, D., Hervás-Martínez, C. (2001). Introducing Multi-objective Optimization in Cooperative Coevolution of Neural Networks. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_77
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DOI: https://doi.org/10.1007/3-540-45720-8_77
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