Summary
Differential evolution (DE) is an evolutionary algorithm designed for global numerical optimization. This chapter presents a new, rotationally invariant DE algorithm that eliminates drift bias from its trial vector generating function by projecting randomly chosen vector differences along lines of recombination. In this way, the natural distribution of vector differences drives both mutation and recombination. The new method also eliminates drift bias from survivor selection, leaving recombination as the only migration pathway. A suite of scalable test functions benchmarks the performance of drift-free DE against that of the algorithm from which it was derived.
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References
Chakraborti, N.: Genetic algorithms and related techniques for optimizing Si–H clusters: A merit analysis for differential evolution. In: Price, K.V., Storn, R.M., Lampinen, J.A. (eds.) Differential evolution: A practical approach to global optimization. Springer, Heidelberg (2005)
Hassani, S.: Mathematical physics: A modern introduction to its foundations. Springer, New York (1999)
Nelder, J.A., Mead, R.: A simplex method for function minimization. Computer Journal 7, 308–313 (1965)
Price, K.V.: An introduction to differential evolution. In: Corne, D., Dorigo, M., Glover, F. (eds.) New ideas in optimization. McGraw-Hill, UK (1999)
Price, K., Storn, R.: Differential evolution. Dr. Dobb’s Journal 78, 18–24 (1997)
Price, K.V., Storn, R.M., Lampinen, J.A.: Differential evolution: A practical approach to global optimization. Springer, Heidelberg (2005)
Price, K.V., Rönkkönen, J.I.: Comparing the unimodal scaling performance of global and local selection in mutation-only differential evolution algorithm. In: Proceedings of 2006 IEEE world congress on computational intelligence, Vancouver, Canada, July 16–21, pp. 7387–7394 (2006) ISBN 0-7803-9489-5
Rudolph, G.: Convergence of evolutionary algorithms in general search spaces. In: Proceedings of the third IEEE conference of evolutionary computation, pp. 50–54. IEEE Press, Los Alamitos (1996)
Salomon, R.: Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions: A survey of some practical and theoretical aspects of genetic algorithms. Biosystems 39(3), 263–278 (1996)
Schwefel, H.-P.: Evolution and optimum seeking. John Wiley and Sons, New York (1995)
Shang, Y.-W., Qiu, Y.-H.: A note on the extended Rosenbrock function. Evolutionary Computation 14(1), 119–126 (2006)
Storn, R., Price, K.: Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)
Vesterstrom, J., Thomsen, R.: A comparative study of differential evolution, particle swarm optimization and evolutionary algorithms on numerical benchmark problems. In: Proceedings of the 2004 congress on evolutionary computation, June 2004, vol. 2(19-23), pp. 1980–1987 (2004)
Whitley, D., Mathias, K., Rana, S., Dzubera, J.: Evaluating evolutionary algorithms. Artificial Intelligence 85, 1–32 (1996)
Yao, X., Liu, Y.: Fast evolution strategies. In: Angeline, P.J., Reynolds, R.G., McDonnell, J.R., Eberhardt, R. (eds.) Evolutionary Programming IV: Proceedings of the sixth international conference, Indianapolis, Indiana, USA, April 1997, pp. 151–161. Springer, Heidelberg (1997)
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Price, K.V. (2008). Eliminating Drift Bias from the Differential Evolution Algorithm. In: Chakraborty, U.K. (eds) Advances in Differential Evolution. Studies in Computational Intelligence, vol 143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68830-3_2
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DOI: https://doi.org/10.1007/978-3-540-68830-3_2
Publisher Name: Springer, Berlin, Heidelberg
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