Abstract
In this paper we propose a new self-adaptative crossover operator for real coded evolutionary algorithms. This operator has the capacity to simulate other real-coded crossover operators dynamically and, therefore, it has the capacity to achieve exploration and exploitation dynamically during the evolutionary process according to the best individuals. In other words, the proposed crossover operator may handle the generational diversity of the population in such a way that it may either generate additional population diversity from the current one, allowing exploration to take effect, or use the diversity previously generated to exploit the better solutions.
In order to test the performance of this crossover, we have used a set of test functions and have made a comparative study of the proposed crossover against other classic crossover operators. The analysis of the results allows us to affirm that the proposed operator has a very suitable behavior; although, it should be noted that it offers a better behavior applied to complex search spaces than simple ones.
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References
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Goldberg, D.: Genetic Algorithm in Search, Optimisation, and Machine Learning. Addison-Wesley, Ann Abor (1989)
Lucasius, C.B., Kateman, G.: Applications of genetic algorithms in chemometrics. In: Proc. of the 3rd Int Conf on Genetic Algorithms, pp. 170–176. Morgan Kaufmann, San Francisco (1989)
Zamparelli, M.: Genetically trained cellular neural networks. Neural Networks 10(6), 1143–1151 (1997)
Oyama, A., Obayashi, S., Nakamura, T.: Real-coded adaptive range genetic algorithm applied to transonic wing optimization. Appl. Soft Computing 1(3), 179–187 (2001)
Roubos, J.A., van Straten, G., van Boxtel, A.J.B.: An evolutionary strategy for fed-batch bioreactor optimization; concepts and performance. Journal Biotechnol. 67(2–3), 173–187 (1999)
Kawabe, T., Tagami, T.: A real coded genetic algorithm for matrix inequality design approach of robust PID controller with two degrees of freedom. In: Proc. of the 1997 IEEE Int. Symp. on Intelligent Control, pp. 119–124 (1997)
Duffy, J., McNelis, P.D.: Approximating and simulating the stochastic growth model: Parameterized expectations, neural networks, and the genetic algorithm. Journal Econ. Dyn. Control 25(9), 1273–1303 (2001)
Harris, S.P., Ifeachor, E.C.: Automatic design of frequency sampling filters by hybrid genetic algorithm techniques. IEEE Trans. Sig. Process. 46(12), 3304–3314 (1998)
Caorsi, S., Massa, A., Pastorino, M.: A computational technique based on a real-coded genetic algorithm for microwave imaging purposes. IEEE Trans. Geosci. Remote Sens. 38(4), 1697–1708 (2000)
Shi, K.L., Chan, T.F., Wong, Y.K., Ho, S.L.: Speed estimation of an induction motor drive using an optimized extended Kalman filter. IEEE Trans. Indust. Electron. 49(1), 124–133 (2002)
Azariadis, P.N., Nearchou, A.C., Aspragathos, N.A.: An evolutionary algorithm for generating planar developments of arbitrarily curved surfaces. Comp. Industry 47(3), 357–368 (2002)
Turcanu, C.O., Craciunescu, T.: A genetic approach to limited data tomographic reconstruction of time-resolved energy spectrum of short-pulsed neutron sources. Pattern Recognition Letter 23(8), 967–976 (2002)
Chang, F.J.: Real-coded genetic algorithm for rule-based flood control reservoir management. Water Resource Management 12(3), 185–198 (1998)
Michalewicz, Z.: Genetics Algorithms + Data Structures = Evolution Programs. WNT, Warsaw (1996)
Herrera, F., Lozano, M., Verdegay, J.L.: Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis. Artificial Intelligence Review 12(4), 265–319 (1998)
Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley, Chichester (2001)
Wright, A.H.: Genetic Algorithms for Real Parameter Optimization. Foundations of genetic algorithms (1991)
Syswerda, G.: Uniform crossover in genetic algorithms. In: Schaffer, J.D. (ed.) ICGA-89, Morgan Kaufmann, San Francisco (1989)
Eshelman, L.J., Caruana, R.A., Schaffer, J.D.: Biases in the Crossover Landscape (1997)
Agrawal, R.B.: Simulated binary crossover for real-coded genetic algorithms. Indian Institute of Technology, Kanpur (1995)
Lozano, M., et al.: Real Coded Memetic Algorithms with Crossover Hill-Climbing. Evolutionary Computation 12(3), 273–302 (2004)
De Jong, K.E.: An analysis of the behavior of a class of genetic adaptive systems. University of Michigan Press, Ann Arbor (1975)
Schwefel, H.P.: Numerical Optimization of Computer Models. Wiley, Chichester (1981)
Torn, A., Zilinskas, A.: Global Optimization. Springer, Berlin (1989)
Griewank, A.O.: Generalized Descent for Global Optimization. Journal of Optimization Theory and Applications 34(1), 11–39 (1981)
Beyer, H.-G., Deb, K.: On self-adaptive features in real-parameter evolutionary algorithms. IEEE Transactions on Evolutionary Computation 5(3), 250–270 (2001)
Kita, H.: A comparison study of self-adaptation in evolution strategies and real-coded genetic algorithms. Evolutionary Computing Journal 9(2), 223–241 (2001)
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Gegúndez, M.E., Palacios, P., Álvarez, J.L. (2007). A New Self-adaptative Crossover Operator for Real-Coded Evolutionary Algorithms. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71618-1_5
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DOI: https://doi.org/10.1007/978-3-540-71618-1_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71589-4
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