Abstract
This chapter proposes the integration of fitness diversity adaptation techniques within the parameter setting of Differential Evolution (DE). The scale factor and crossover rate are encoded within each genotype and self-adaptively updated during the evolution by means of a probabilistic criterion which takes into account the diversity properties of the entire population. The population size is also adaptively controlled by means of a novel technique based on a measurement of the fitness diversity. An extensive experimental setup has been implemented by including multivariate problems and hard to solve fitness landscapes. A comparison of the performance has been conducted by considering both standard DE and modern DE based algorithms, recently proposed in the literature. Available numerical results show that the proposed approach seems to be very promising for some fitness landscapes and still competitive with modern algorithms in other cases. In most cases analyzed the proposed self-adaptation is beneficial in terms of algorithmic performance and can be considered a useful tool for enhancing the performance of a DE scheme.
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References
Ali, M.M., Törn, A.: Population set based global optimization algorithms: Some modifications and numerical studies. Computers and Operations Research 31(10), 1703–1725 (2004)
Brest, J., Greiner, S., Boskovic, B., Mernik, M., Žumer, V.: Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation 10(6), 646–657 (2006)
Brest, J., Zumer, V., Maucec, M.: Self-adaptive differential evolution algorithm in constrained real-parameter optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 215–222 (2006)
Caponio, A., Cascella, G.L., Neri, F., Salvatore, N., Sumner, M.: A fast adaptive memetic algorithm for on-line and off-line control design of PMSM drives. IEEE Transactions on System Man and Cybernetics-part B, special issue on Memetic Algorithms 37(1), 28–41 (2007)
Caponio, A., Neri, F., Tirronen, V.: Super-fit control adaptation in memetic differential evolution frameworks. Soft Computing-A Fusion of Foundations, Methodologies and Applications 13(8), 811–831 (2009)
Chakraborty, U.K. (ed.): Advances in Differential Evolution. Studies in Computational Intelligence, vol. 143. Springer, Heidelberg (2008)
Chakraborty, U.K., Das, S., Konar, A.: Differential evolution with local neighborhood. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 2042–2049 (2006)
Das, S., Abraham, A., Chakraborty, U.K., Konar, A.: Differential evolution with a neighborhood-based mutation operator. IEEE Transactions on Evolutionary Computation (to appear, 2009)
Deb, K.: Multi-objective Optimization using Evolutionary Algorithms, pp. 147–149. Wiley and Sons LTD, Chichester (2001)
Eiben, A.E., Schippers, C.A.: On evolutionary exploration and exploitation. Fundamenta Informaticae 35(1-4), 35–50 (1998)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computation. Springer, Berlin (2003)
Feoktistov, V.: Differential Evolution in Search of Solutions, pp. 83–86. Springer, Heidelberg (2006)
Gämperle, R., Müller, S.D., Koumoutsakos, P.: A parameter study for differential evolution. In: NNA-FSFS-EC, WSEAS, pp. 293–298 (2002)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Co., Reading (1989)
Krasnogor, N.: Toward robust memetic algorithms. In: Hart, W.E., Krasnogor, N., Smith, J.E. (eds.) Recent Advances in Memetic Algorithms. Studies in Fuzzines and Soft Computing, pp. 185–207. Springer, Berlin (2004)
Lampinen, J., Zelinka, I.: On stagnation of the differential evolution algorithm. In: Osmera, P. (ed.) Proceedings of 6th International Mendel Conference on Soft Computing, pp. 76–83 (2000)
Liu, J., Lampinen, J.: A fuzzy adaptive differential evolution algorithm. In: Proceedings of the 17th IEEE region 10 international conference on computer, communications, control and power engineering, vol. I, pp. 606–611 (2002)
Liu, J., Lampinen, J.: On setting the control parameter of the differential evolution algorithm. In: Proceedings of the 8th international Mendel conference on soft computing, pp. 11–18 (2002)
Liu, J., Lampinen, J.: A fuzzy adaptive differential evolution algorithm. Soft Computing - A Fusion of Foundations, Methodologies and Applications 9(6), 448–462 (2005)
Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Tech. Rep. 790 (1989)
Neri, F., Mäkinen, R.A.E.: Hierarchical evolutionary algorithms and noise compensation via adaptation. In: Yang, S., Ong, Y.S., Jin, Y. (eds.) Evolutionary Computation in Dynamic and Uncertain Environments. Studies in Computational Intelligence, ch. 15, pp. 345–369. Springer, Heidelberg (2007)
Neri, F., Toivanen, J., Cascella, G.L., Ong, Y.S.: An adaptive multimeme algorithm for designing HIV multidrug therapies. IEEE/ACM Transactions on Computational Biology and Bioinformatics, Special Issue on Computational Intelligence Approaches in Computational Biology and Bioinformatics 4(2), 264–278 (2007)
Neri, F., Toivanen, J., Mäkinen, R.A.E.: An adaptive evolutionary algorithm with intelligent mutation local searchers for designing multidrug therapies for HIV. Applied Intelligence, Special Issue on Computational Intelligence in Medicine and Biology 27(3), 219–235 (2007)
Neri, F., Kotilainen, N., Vapa, M.: A memetic-neural approach to discover resources in P2P networks. In: van Hemert, J., Cotta, C. (eds.) Recent Advances in Evolutionary Computation for Combinatorial Optimization. Studies in Computational Intelligence, ch. 8, pp. 119–136. Springer, Heidelberg (2008)
NIST/SEMATECH, e-Handbook of Statistical Methods (2003), http://www.itl.nist.gov/div898/handbook/
Noman, N., Iba, H.: Accelerating differential evolution using an adaptive local search. IEEE Transactions on Evolutionary Computation 12(1), 107–125 (2008)
Ong, Y.S., Keane, A.J.: Meta-lamarkian learning in memetic algorithms. IEEE Transactions on Evolutionary Computation 8(2), 99–110 (2004)
Price, K., Storn, R.: Differential evolution: A simple evolution strategy for fast optimization. Dr Dobb’s J. Software Tools 22(4), 18–24 (1997)
Price, K.V., Storn, R., Lampinen, J.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Heidelberg (2005)
Qin, A.K., Suganthan, P.N.: Self-adaptive differential evolution algorithm for numerical optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 2, pp. 1785–1791 (2005)
Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.: Opposition-based differential evolution. IEEE Transactions on Evolutionary Computation 12(1), 64–79 (2008)
Rechemberg, I.: Evolutionstrategie: Optimierung Technisher Systeme nach prinzipien des Biologishen Evolution. Fromman-Hozlboog Verlag (1973)
Storn, R., Price, K.: Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. Tech. Rep. TR-95-012, ICSI (1995)
Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal on Global Optimization 11, 341–359 (1997)
Sun, J., Zhang, Q., Tsang, E.: DE/EDA: A new evolutionary algorithm for global optimization. Information Science (169), 249–262 (2004)
Teo, J.: Exploring dynamic self-adaptive populations in differential evolution. Soft Computing–A Fusion of Foundations, Methodologies and Applications 10(8), 673–686 (2006)
Tirronen, V., Neri, F., Kärkkäinen, T., Majava, K., Rossi, T.: A memetic differential evolution in filter design for defect detection in paper production. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 320–329. Springer, Heidelberg (2007)
Tirronen, V., Neri, F., Kärkkäinen, T., Majava, K., Rossi, T.: An enhanced memetic differential evolution in filter design for defect detection in paper production. Evolutionary Computation 16(4), 529–555 (2008)
Tsutsui, S., Yamamura, M., Higuchi, T.: Multi-parent recombination with simplex crossover in real coded genetic algorithms. In: Proceedings of the Genetic Evol. Comput. Conf (GECCO), pp. 657–664 (1999)
Wolpert, D., Macready, W.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67–82 (1997)
Yang, Z., He, J., Yao, X.: Making a difference to differential evolution. In: Michalewicz, Z., Siarry, P. (eds.) Advances in Metaheuristics for Hard Optimization, pp. 397–414 (2008)
Yang, Z., Tang, K., Yao, X.: Self-adaptive differential evolution with neighborhood search. In: Proceedings of the World Congress on Computational Intelligence, pp. 1110–1116 (2008)
Zamuda, A., Brest, J., Boskovic, B., Zumer, V.: Differential evolution for multiobjective optimization with self adaptation. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 3617–3624 (2007)
Zielinski, K., Weitkemper, P., Laur, R., Kammeyer, K.D.: Parameter study for differential evolution using a power allocation problem including interference cancellation. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1857–1864 (2006)
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Tirronen, V., Neri, F. (2009). Differential Evolution with Fitness Diversity Self-adaptation. In: Chiong, R. (eds) Nature-Inspired Algorithms for Optimisation. Studies in Computational Intelligence, vol 193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00267-0_7
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DOI: https://doi.org/10.1007/978-3-642-00267-0_7
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