Abstract
An adaptive genetic algorithm based on information entropy is presented in this paper. Unlike traditionally approach, the proposed AGA let the crossover- and mutation- rate optimized by GA itself and user need not confirm the concrete values of the two parameters. Hence, it greatly decreases the workload for iterative debugging the corresponding parameters. As a modified algorithm, this AGA has the following holistic characters: (1) the quasi-exact penalty function is developed to solve nonlinear programming (NLP) problems with equality and inequality constraints, (2) entropy-based searching technique with narrowing down space is taken to speed up the convergence, (3) a specific strategy of reserving the most fitness member with evolutionary historic information is effectively used to approximate the solution of the nonlinear programming problems to the global optimization, (4) A new adaptive strategy is employed to overcome the difficulty in confirming the genetic parameters, (5) a new iteration scheme is used in conjunction with multi-population genetic strategy to terminate the evolution procedure appropriately. Numerical examples and the performance test show that the proposed method has good accuracy and efficiency.
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© 2005 Springer-Verlag Berlin Heidelberg
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Sun, Y., Li, Cl., Wang, Ag., Zhu, J., Wang, Xc. (2005). An Adaptive GA Based on Information Entropy. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_11
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DOI: https://doi.org/10.1007/11539902_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28320-1
Online ISBN: 978-3-540-31863-7
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