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Paper VIII.1

General Genetic Algorithms and Simulated Annealing Perturbation of the Gradient Method with a Fixed Parameter

doi:10.4203/ccp.34.8.1

J.E. Souza de Cursi, M.B.S. Cortes, "General Genetic Algorithms and Simulated Annealing Perturbation of the Gradient Method with a Fixed Parameter", in B.H.V. Topping, (Editor), "Developments in Neural Networks and Evolutionary Computing for Civil and Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 189-198, 1995. doi:10.4203/ccp.34.8.1

Abstract

This paper considers a situation which is very often found in Engineering Sciences: the problem of finding a global minimum of a differentiable functional J on a ball B. We are interested in the prevention of convergence to local minima by using random perturbations of usual descent methods and the use of information about the gradient in genetic algorithms. We shall that such an information about the gradient can be introduced as a kind of mutation. In this case, a complete theory can be established and we obtain a mathematical result of convergence to a global minimum, for suitable random perturbations. The results of some numerical experiments are given.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 34 DEVELOPMENTS IN NEURAL NETWORKS AND EVOLUTIONARY COMPUTING FOR CIVIL AND STRUCTURAL ENGINEERING Edited by: B.H.V. Topping Paper VIII.1 General Genetic Algorithms and Simulated Annealing Perturbation of the Gradient Method with a Fixed Parameter J.E. Souza de Cursi and M.B.S. Cortes I.M.R. - INSA de Rouen, France doi:10.4203/ccp.34.8.1 purchase the full-text of this paper Full Bibliographic Reference for this paper J.E. Souza de Cursi, M.B.S. Cortes, "General Genetic Algorithms and Simulated Annealing Perturbation of the Gradient Method with a Fixed Parameter", in B.H.V. Topping, (Editor), "Developments in Neural Networks and Evolutionary Computing for Civil and Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 189-198, 1995. doi:10.4203/ccp.34.8.1 Abstract This paper considers a situation which is very often found in Engineering Sciences: the problem of finding a global minimum of a differentiable functional J on a ball B. We are interested in the prevention of convergence to local minima by using random perturbations of usual descent methods and the use of information about the gradient in genetic algorithms. We shall that such an information about the gradient can be introduced as a kind of mutation. In this case, a complete theory can be established and we obtain a mathematical result of convergence to a global minimum, for suitable random perturbations. The results of some numerical experiments are given. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £55 +P&P)
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