Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ali MM, Törn A (2000) Optimization of carbon and silicon cluster geometry for Tersoff potential using differential evolution. In: Floudas CA, Pardalos PM (eds) Optimisation in computational chemistry and molecular biology. Kluwer Academic, Dordrecht, pp 1–15
Bäck T (1993) Optimal mutation rates in genetic search. In: Forrest F (ed) Proceedings of the fifth international conference on genetic algorithms. Morgan-Kaufmann, San Mateo, CA, pp 2–8
Bäck T (1996) Evolutionary algorithms in theory and in practice. Oxford University Press
Bäck T, Schwefel H-P (1993) An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation 1(1):1–23
Bäck T, Schwefel H-P (1995) Evolution strategies I: variants and their computational implementation. In: Periaux J, Winter G (eds) Genetic algorithms in engineering and computer science. Wiley, Chichester, Chap. 6
Bäck T, Rudolph G, Schwefel H-P (1993) Evolutionary programming and evolution strategies: similarities and differences. In: Fogel DG, Atmar W (eds) Second annual conference on evolutionary programming, February. Evolutionary Programming Society, La Jolla, CA, pp 11–22
Baker JE (1987) Reducing bias and inefficiency in the selection algorithm. In: Grenfenstette JJ (ed) Proceedings of the first international conference on genetic algorithms and their applications. Lawrence Erlbaum, Hillsdale, NJ, pp 14–21
Beckman FS (1980) Mathematical foundations of programming. Addison-Wesley, Reading, MA
Beyer HG (1999) On the dynamics of EAs without selection. In: Banzaf W, Reeves C (eds) Foundations of genetic algorithms. Morgan Kaufmann, San Mateo, CA, pp 5–26
Blahut RE (1984) Fast algorithms for digital signal processing. Addison-Wesley, Reading, MA, p 329
Caruana RA, Eshelman LJ, Schaffer JD (1989) Representation and hidden bias II: eliminating defining length bias in genetic search via shuffle crossover. In: Sidharan NS (ed) Eleventh international joint conference on artificial intelligence, Morgan Kaufmann, San Mateo, CA, vol 1, pp 750–755
Chakraborti N, Misra K, Bhatt P, Barman N, Prasad R (2001) Tight-binding calculations of Si-H clusters using genetic algorithms and related techniques: studies using differential evolution. Journal of Phase Equilibria 22(5):525–530
Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, Berlin Heidelberg New York
Eshelman LJ, Caruana RA, Schaffer JD (1989) Biases in the crossover landscape. In: Schaffer JD (ed) Proceedings of the third international conference on genetic algorithms. Morgan Kaufmann, San Francisco, pp 10–19
Fogel DB (1991) System identification through simulated evolution: a machine learning approach to modeling. Ginn Press, Needham Heights, MA
Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, New York
Gamperle G, Mueller SD, Koumoutsakos P (2002) A parameter study for differential evolution. In: Grmela A, Mastorakis NE (eds) Advances in intelligent systems, fuzzy systems, evolutionary computation. WSEAS Press, Athens, pp 293–298
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading, MA
Halton J, Weller G (1964) Algorithm 247: radical inverse quasi-random point sequence. Communications of the ACM, 7(12):701–702
Holland, JH (1973) Genetic algorithms and the optimal allocation of trials. SIAM Journal of Computing 2:88–105
Holland J (1992) Adaptation in natural and artificial systems. MIT Press, Cambridge, MA. First edition 1975, The University of Michigan Press, Ann Arbor
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the international conference on evolutionary computation, Perth, Australia. Invited paper
Koza J (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA
Lampinen J (2002) Multi-constrained nonlinear optimization by the differential evolution algorithm. In: Rajkumar Roy, Mario Köppen, Seppo Ovaska, Takeshi Furuhashi, Frank Hoffmann (eds) Soft computing and industry: recent advances. Springer, Berlin Heidelberg New York, pp 305–318 (Proceedings of the 6th online world conference on soft computing in industrial applications (WSC6), September 10–24, 2001. Available at: http://vision.fhg.de/wsc6)
Lampinen J, Zelinka I (2000) On stagnation of (the) differential evolution algorithm. In: Ošmera P (ed) Proceedings of MENDEL 2000, sixth international Mendel conference on soft computing, June 7–9, Brno, Czech Republic. Brno University of Technology, Faculty of Mechanical Engineering, Institute of Automation and Computer Science, Brno, pp 76–83. Available at: http://www.lut.fi/~jlampine/MEND2000.ps
Macready WG, Wolpert DH (1998) Bandit problems and the exploration/exploitation tradeoff. IEEE Transactions on Evolutionary Computing 2(1):2–22
Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs, 3rd edn. Springer, Berlin Heidelberg New York
Mühlenbein H (1992) How genetic algorithms really work I: mutation and hill climbing. In: Schwefel H-P, Männer R (eds) Proceedings of the second international conference on parallel problem solving from nature, Springer, Berlin Heidelberg New York, pp 15–26
Mühlenbein H, Schlierkamp-Voosen D (1993) Predictive models for the breeder genetic algorithm. Evolutionary Computation 1(1):25–50
Peitgen H-O, Saupe D (eds) (1998) The science of fractal images. Springer, Berlin Heidelberg New York
Potter MA, DeJong KA (1994) A cooperative co-evolutionary approach to function optimization. In: Davidor Y, Schwefel H-P, Männer R (eds) Proceedings of parallel problems solving from nature 3. Springer, Berlin Heidelberg New York, pp 249–257
Price KV (1996) Differential evolution: a fast and simple numerical optimizer. In: Smith MH, Lee MA, Keller J, Yen J (eds) Proceedings of the 1996 biennial conference of the North American fuzzy information processing society — NAFIPS, June 19–22, Berkeley, CA, USA. IEEE Press, New York, pp 524–527
Price KV (1997) Differential evolution vs. the functions of the second ICEO. In: Proceedings of the 1997 IEEE international conference on evolutionary computation, Indianapolis, Indiana, USA. IEEE Press, New York, pp 153–157
Rudolph G (1996) Convergence of evolutionary algorithms in general search spaces. In: Proceedings of the third IEEE conference on evolutionary computation, IEEE Press, New York, pp 50–54
Saravanan N, Fogel DB (1997) Multi-operator evolutionary programming: a preliminary study on function optimization. In: Angeline PJ, Reynolds RG, McDonnell JR, Eberhart R (eds) Evolutionary programming 6: sixth international conference, Indianapolis, Indiana, USA, April. Springer, Berlin Heidelberg New York, pp 215–221
Salomon R (1996a) Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions: a survey of some theoretical and practical aspects of genetic algorithms. Biosystems 39(3):263–278
Salomon R (1996b) The influence of different coding schemes on the computational complexity of genetic algorithms in function optimization. In: Voigt HM, Ebeling E, Rechenberg I, Schwefel H-P (eds) Proceedings of the fourth international conference on parallel problem solving from nature, Springer, Berlin Heidelberg New York, pp 227–235
Salomon R (1997) Raising theoretical questions about the utility of genetic algorithms. In: Proceedings of the sixth international conference on evolutionary programming. Lecture notes in computer science, vol 1213. Springer, Berlin Heidelberg New York, pp 275–284
Spears WM, DeJong KA (1991) An analysis of multi-point crossover. In: Rawlins G (ed) Foundations of genetic algorithms. Morgan Kaufmann, San Francisco, pp. 301–315
Storn R (1996) On the usage of differential evolution for function optimization. In: Smith MH, Lee MA, Keller J, Yen J (eds) Proceedings of the 1996 biennial conference of the North American fuzzy information processing society — NAFIPS, June 19–22, Berkeley, CA, USA. IEEE Press, New York, pp 519–523
Storn R, Price KV (1996) Minimizing the real functions of the ICEC’96 contest by differential evolution. In: Proceedings of the 1996 IEEE international conference on evolutionary computation, Nagoya, Japan. IEEE Press, New York, pp 842–844
Storn R, Price KV (1997) Differential evolution — a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11:341–359
Syswerda G (1989) Uniform crossover in genetic algorithms. In: Schaffer JD (ed) Proceedings of the third international conference on genetic algorithms. Morgan Kaufmann, San Francisco, pp 2–9
Wright AH (1991) Genetic algorithms for real parameter optimization. In: Rawlins GJE (ed) Foundations of genetic algorithms. Morgan-Kaufmann, San Mateo, CA, pp 205–218
Yang J-M, Chen Y-P, Horng J-T, Kao C-M, Chen Y-P, Horng J-T, Kao C-Y (1997) Applying family competition to evolution strategies for constrained optimization. In: Angeline PJ, Reynolds RG, McDonnell JR, Eberhart R (eds) Evolutionary programming 6: sixth international conference, Indianapolis, Indiana, USA, April. Springer, Berlin Heidelberg New York, pp 201–211
Yao X, Liu Y (1997) Fast evolution strategies. In: Angeline PJ, Reynolds RG, McDonnell JR and Eberhart R (eds) Proceedings of the sixth international conference on evolutionary programming, Springer, Berlin Heidelberg New York, pp 151–161
Zaharie D (2002) Critical values for the control parameters of differential evolution algorithms. In: Matoušek R, Ošmera P (eds) Proceedings of MENDEL 2002, 8th international conference on soft computing, June 5–7, 2002, Brno, Czech Republic. Brno University of Technology, Faculty of Mechanical Engineering, Institute of Automation and Computer Science, Brno, pp 62–67
Zimmons P (n.d.) Polynomial fitting with differential evolution. Available at: http://www.cs.unc.edu/~zimmons/cs258/poly.html
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
(2005). The Differential Evolution Algorithm. In: Differential Evolution. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31306-0_2
Download citation
DOI: https://doi.org/10.1007/3-540-31306-0_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20950-8
Online ISBN: 978-3-540-31306-9
eBook Packages: Computer ScienceComputer Science (R0)