The optimal combination: Grammatical swarm,particle swarm optimization and neural networks |
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| Authors: | Luis Fernando de Mingo López Nuria Gómez Blas Alberto Arteta |
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| Institution: | 1. School of Mechanical and Electronic Engineering, Foshan University, Foshan 528000, China;2. Key Laboratory of Systems & Control, Academy of Mathematics & Systems Science, Chinese Academy of Sciences, Beijing 100190, China;1. Virtual Plants, INRIA Sophia-antipolis, France;2. CIRAD/UMR AGAP, Avenue Agropolis, TA 40/02, 34398Montpellier, France;3. Parietal project-team, INRIA Saclay-île de France, France;4. CEA/Neurospin bât 145, 91191 Gif-Sur-Yvette, France;5. Simula Research Laboratory, P.O. Box 134 NO-1325 Lysaker, Norway;6. Department of Informatics, University of Oslo, P.O. Box 1080 Blinder, NO-0316, Norway;1. School of Information and Electrical Engineering, Ludong University, Yantai 264025, China;2. Shandong Institute of Business and Technology, Yantai 264005, China;3. Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China;1. Department of Physics and Astronomy, West Virginia University, PO Box 6315, Morgantown, WV 26506, USA;2. Spaces Sciences Laboratory, University of California, Berkeley, CA 94720, USA;3. ASTRON, PO Box 2, 7990 AA Dwingeloo, The Netherlands;4. Department of Astrophysics, Radboud University, PO Box 9010, 6500 GL Nijmegen, The Netherlands;5. NRAO, Green Bank Observatory, PO Box 2, Green Bank, WV 24944, USA |
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| Abstract: | Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks. |
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