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ALENA. Adaptive-Length Evolving Neural Arrays
Keywords:
Evolving Neural Networks, Evolving Neural Arrays, Learning, Genetic Algorithms, SubpopulationsAbstract
Evolving neural arrays (ENA) have proved to be capable of learning complex behaviors, i.e., problems whose solution requires strategy learning. For this reason, they present many applications in various areas such as robotics and process control. Unlike conventional methods "based on a single neural network" ENAs are made up of a set of networks organized as an array. Each of them represents a part of the expected solution. This work describes a new method, ALENA, that enhances the solutions obtained by solving the main deficiencies of ENA since it eases the obtaining of specialized components, does not require the explicit decomposition of the problem into subtasks, and is capable of automatically adjusting the arrays length for each particular use. The measurements of the proposed method "applied to problems of obstacle evasion and objects collection" show the superiority of ALENA in relation to the traditional methods that deal with populations of neural networks. SANE has been used in particular as a comparative referent due to its high performance. Eventually, conclusions and some future lines of work are presented.
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References
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[11] Moriarty, D. E. & Miikkulainen, R. Forming Neural Networks Through Efficient and Adaptive Coevolution. Information Sciences Institute, University of Southern California. Department of Computer Sciences, The University of Texas at Austin. IEEE Transactions on Evolutionary Computation. Vol.5, (1997), pp.373-399.
[12] Yao, X. and Liu, Y. Ensemble Structure of Evolutionary Artificial Neural networks. Computational intelligence Group, School of Computer Science University College. Australian Defense Force Academy, Canberra, ACT, Australia 2600. 1996.
[13] Yao, X. and Liu, Y. A New Evolutionary System for Evolving Artificial Neural Networks. IEEE Transactions on Neural Networks, Vol 8, nro. 3, pp 694-713, 1997
[14] Yao, X. Evolving Artificial Neural networks. School of Computer Science The University of Birmingham Edgbaston, Birmingham B15 2TT. Proceedings of the IEEE. Vol.87, No.9, (September 1999), pp.1423-1447.
[2] Corbalán, L., Pisano, M., Osella Massa, G. y Lanzarini L. Criaturas Virtuales especificadas a través de Redes Neuronales Evolutivas. VII Congreso Argentino de Ciencias de la Computación. Argentina, Vol. 2, (Octubre 2001), pp. 1105-1115.
[3] Corbalán Leonardo. Evolución de redes neuronales para comandar criaturas que alcanzan objetivos sorteando obstáculos en un entorno virtual 2D. Tesis de grado correspondiente a la carrera Lic. en Informática. UNLP. Marzo 2002.
[4] Corbalán Leonardo y Lanzarini Laura. Arreglos neuronales evolutivos aplicados a la evasión de obstáculos y alcance de objetivos. VIII Congreso Argentino de Ciencias de la Computación CACIC 2002 (octubre 2002) - XXVIII Conferencia Latinoamericana de Informática CLEI 2002 (noviembre 2002).
[5] Corbalán Leonardo y Lanzarini Laura. An ENA-Based Strategy Replacing Subobjectives Definition in Incremental Learning. International Conference on Computer Science, Software Engineering, Information Technology, e-Business, and Applications, (CSITeA’03). Junio 2003, Río de Janeiro, Brasil.
[6] Freeman, J. A. & Skapura, D. M. Redes neuronales Algoritmos, aplicaciones y técnicas de programación. Addison–Wesley, 1991. Versión en español de: Rafael García -Bermejo Giner. Addison–Wesley Iberoamericana 1993.
[7] Goldberg, D. E. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison–Wesley, 1989. pág. 10
[8] Gomez, F. and Miikkulainen, R. Incremental Evolution Of Complex General Behavior Department of Computer Sciences, The University of Texas at Austin. Adaptive Behavior. Vol 5, (1997), pp.317-342.
[9] Moriarty, D. E. & Miikkulainen, R. Efficient Reinforcement Learning through Symbiotic Evolution. Department of Computer Sciences, The University of Texas at Austin. Austin, TX 78712. Machine Learning, Vol. 22, (1996), pp.11-33.
[10] Moriarty, D. E. Symbiotic Evolution of Neural Networks in Sequential Decision Tasks. Department of Computer Sciences, The University of Texas at Austin Ph.D. Dissertation. Technical Report AI97-257, January 1997.
[11] Moriarty, D. E. & Miikkulainen, R. Forming Neural Networks Through Efficient and Adaptive Coevolution. Information Sciences Institute, University of Southern California. Department of Computer Sciences, The University of Texas at Austin. IEEE Transactions on Evolutionary Computation. Vol.5, (1997), pp.373-399.
[12] Yao, X. and Liu, Y. Ensemble Structure of Evolutionary Artificial Neural networks. Computational intelligence Group, School of Computer Science University College. Australian Defense Force Academy, Canberra, ACT, Australia 2600. 1996.
[13] Yao, X. and Liu, Y. A New Evolutionary System for Evolving Artificial Neural Networks. IEEE Transactions on Neural Networks, Vol 8, nro. 3, pp 694-713, 1997
[14] Yao, X. Evolving Artificial Neural networks. School of Computer Science The University of Birmingham Edgbaston, Birmingham B15 2TT. Proceedings of the IEEE. Vol.87, No.9, (September 1999), pp.1423-1447.
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Published
2004-04-01
How to Cite
Corbalán, L. C., Lanzarini, L. C., & De Giusti, A. E. (2004). ALENA. Adaptive-Length Evolving Neural Arrays. Journal of Computer Science and Technology, 4(01), p. 59–65. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/915
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