Skip to main content

Benchmarking Differential Evolution

  • Chapter
Differential Evolution

Part of the book series: Natural Computing Series ((NCS))

  • 4161 Accesses

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Ackley DH (1987) A connectionist machine for genetic hillclimbing. Kluwer, Boston, MA, USA

    Google Scholar 

  • Ali MM, Törn A (1998) Evolution based global optimization techniques and the controlled random search algorithm: Proposed modifications and numerical studies. Submitted to the Journal of Global Optimization, 1998, Kluwer Academic Publishers, The Netherlands

    Google Scholar 

  • Ali MM, Törn A (2000) Optimization of carbon and silicon clusters geometry for Tersoff potential using differential evolution. In: Floudas CA, Pardalos PM (eds) Optimization in computational and molecular biology. Kluwer Academic Publishers pp 1–15

    Google Scholar 

  • Aluffi-Pentini F, Parisi V, Zirilli F (1985) Global optimization and stochastic differential equations. Journal of Optimization and Theory and Applications 47(1):1–16

    Article  MathSciNet  MATH  Google Scholar 

  • Angeline PJ (1998) Evolutionary optimization versus particle swarm optimization. In: Porto VW, Saravanan N, Waagen D, Eiben AE (eds) Evolutionary programming VII. Springer, Berlin pp 601–610

    Google Scholar 

  • Babu BV, Sastry KKN (1999) Estimation of heat transfer parameters in a tricklebed reactor using differential evolution and orthogonal collocation. Computers and Chemical Engineering 23:327–339

    Article  Google Scholar 

  • Bersini H, Dorigo M, Langerman S, Seront G, Gambardella L (1996) Results of the first international contest on evolutionary optimization (1st ICEO). In: Proceedings of the 1996 international conference on evolutionary computation, Nagoya, Japan, May 20–22. IEEE Press

    Google Scholar 

  • Cao YJ, Wu QH (1997) Mechanical design optimization by mixed-variable evolutionary programming. In: Proceedings of the 1997 conference on evolutionary computation. IEEE Press pp 443–446

    Google Scholar 

  • Chellapilla K (1998) Combining mutation operators in evolutionary programming. IEEE Transactions on Evolutionary Computation 2:91–96

    Article  Google Scholar 

  • Chen JL, Tsao YC (1993) Optimal design of machine elements using genetic algorithms. Journal of the Chinese Society of Mechanical Engineers 14(2):193–199

    Google Scholar 

  • Corana A, Marchesi M, Martini C, Ridella S (1987) Minimizing multimodal functions for continuous variables with the “simulated annealing algorithm”. ACM Transactions on Mathematical Software, March 1987, pp 272–280

    Google Scholar 

  • Crutchley DA, Zwolinski M (2003) Globally convergent algorithms for DC operating point analysis for nonlinear circuits. IEEE Transactions on Evolutionary Computation 7(1):2–10

    Article  Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6:182–197

    Article  Google Scholar 

  • Fischer MM, Reismann M, Hlavackova-Schindler K (1999) Parameter estimation in neural spatial interaction modelling by a derivative free global optimization method. In: Proceedings of IV international conference on geocomputation, Mary Washington College, Fredericksburg, VA, USA, July 25–28, 1999 Available via Internet: http://www.geovista.psu.edu/sites/geocomp99/Gc99/007/gc_007.htm

    Google Scholar 

  • Fu J-F, Fenton RG, Cleghorn WL (1991) A mixed integer-discrete-continuous programming method and its application to engineering design optimization. Engineering Optimization 17(4):263–280

    Google Scholar 

  • Goodman R, Zeng Z (1994) A learning algorithm for multi-layer perceptrons with hard-limiting threshold units. In: Proceedings of the IEEE Neural Networks for Signal Processing, pp 219–228

    Google Scholar 

  • Gorwin EM, Logar AM, Oldham WJB (1994) An iterative method for training multilayer networks with threshold functions. IEEE Transactions on Neural Networks 5:507–508

    Article  Google Scholar 

  • Griewangk AO (1981) Generalized descent for global optimization. JOTA 34:11–39

    Article  Google Scholar 

  • Han K-H, Kim J-H (2004) Quantum-inspired evolutionary algorithms with a new termination criterion, Hɛ gate, and two-phase scheme. IEEE transactions on Evolutionary Computation 8(2):156–169

    Article  Google Scholar 

  • Hu YF, Mcguire KC, Cokljat D, Blake RJ (1997) Parallel controlled random search algorithms for shape optimization. In: Emerson DR, Ecer A, Periaux J, Satofuka N (eds) Parallel computational fluid dynamics. North-Holland, pp 345–352

    Google Scholar 

  • Ingber L (1993) Simulated annealing: Practice versus theory. Journal of Mathematical and Computer Modeling 18(11):29–57

    Article  MATH  MathSciNet  Google Scholar 

  • Joines JA, Houck CR (1994) On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems. In: Proceedings of the first IEEE conference on evolutionary computation, June 27–29. IEEE Press vol 2, pp 579–584

    Article  Google Scholar 

  • Joshi R, Sanderson AC (1999) Minimal representation multisensor fusion using differential evolution. IEEE Transactions on systems, man and cybernetics — part A: Systems and Humans 29(1):63–76

    Article  Google Scholar 

  • Katsuura H (1991) Continuous nowhere differential functions — an application of contraction mappings. The American Mathematical Monthly 5(98)

    Google Scholar 

  • Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE international conference on neural networks, 4. IEEE Press, Piscataway, NJ, USA pp 1942–1948

    Google Scholar 

  • Krink T, Filipie B, Fogel GB (2004) Noisy optimization problems — a particular challenge for differential evolution? In: Proceedings of the 2004 Congress on evolutionary computation vol 1, pp 332–339

    Article  Google Scholar 

  • Kozeil S, Michalewicz Z (1999) Evolutionary algorithms, homomorphous mappings and constrained parameter optimization. Evolutionary Computation 7(1):19–44

    Google Scholar 

  • Kukkonen S, Lampinen J (2004) An extension of generalized differential evolution for multi-objective optimization with constraints. In: Proceedings of PPSN 2004, the 8th International conference on parallel problem solving from nature, September 18–22 2004, Birmingham, UK, pp 752–761. Springer, ISBN: 3-540-23092-0

    Google Scholar 

  • Lampinen J (2002). A constraint handling approach for the differential evolution algorithm. In: Proceedings of the 2002 IEEE world congress on computational intelligence — WCCI 2002, 2002 Congress on evolutionary computation — CEC 2002, Honolulu, Hawaii, May 12—17, 2002. IEEE Press, 6 pages. ISBN 0-7803-7281-6

    Google Scholar 

  • Lampinen J, Storn R (2004) Differential evolution. In: Onwubolu GC, Babu BV (eds) New optimization techniques in engineering. Studies in fuzziness and soft computing, vol 141, Chapter 6. Springer, pp 123–166. ISBN 3-540-20167-X

    Google Scholar 

  • Lampinen J, Zelinka I (1999) Mechanical engineering design optimization by differential evolution. In: Corne D, Dorigo M, Glover F (eds) New ideas in optimization. McGraw-Hill, Maidenhead, UK pp 127–146

    Google Scholar 

  • Lee C-Y, Yao X (2004) Evolutionary programming using mutations based on the Levy probability distribution. IEEE Transactions on Evolutionary Computation 8(1):1–13

    Article  Google Scholar 

  • Li HL, Chow CT (1994) A global approach for nonlinear mixed discrete programming in design optimization. Engineering Optimization 22:109–122

    Google Scholar 

  • Leung Y-W, Wang Y (2001) An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Transactions on Evolutionary Computation 5(1):41–53

    Article  Google Scholar 

  • Lin SS, Zhang C, Wang H-P (1995) On mixed-discrete nonlinear optimization problems: A comparative study. Engineering Optimization 23(4):287–300

    Google Scholar 

  • Loh HT, Papalambros PY (1991) A sequential linearization approach for solving mixed-discrete nonlinear design optimization problems. Transactions of the ASME, Journal of Mechanical Design 113(3):325–334

    Google Scholar 

  • Loh HT, Paplambros PY (1991a) Computational implementation and tests of a sequential linearization algorithm for mixed-discrete nonlinear design optimization. Transactions of the ASME, Journal of Mechanical Design 113(3):335–345

    Google Scholar 

  • Margoulas GD, Vrahatis MN, Grapsa TN, Androulackis GS (1997) A training method for discrete multilayer neural networks. In: Ellacot SW, Mason JC, Anderson IJ (eds) Mathematics of neural networks: Models, algorithms and applications, chapter 41. Kluwer Academic Publishers

    Google Scholar 

  • Michalewicz Z (1995) Genetic algorithms, numerical optimization and constraints. In: Proceedings of the sixth international conference on genetic algorithms, Pittsburgh, July 15–19 pp 151–158

    Google Scholar 

  • Michalewicz Z, Schoenauer M (1996) Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Computation 4(1):1–32

    Google Scholar 

  • Moscato PA (1989) On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Technical report, ??Caltech concurrent computation program report 826, Caltech, Pasadena, California

    Google Scholar 

  • Mühlenbein H, Scomisch D, Born J (1991) The parallel genetic algorithm as function optimizer. Parallel Computing 17:619–632

    Article  MATH  Google Scholar 

  • Mühlenbein H, Schlierkamp-Vosen D (1993) Predictive models for the breeder genetic algorithm, I. Continuous parameter optimization. Evolutionary Computation 1(1):25–49

    Google Scholar 

  • Paterlini S, Krink T (2004) Differential evolution and particle swarm optimization in partitional culstering. In: Proceedings of the 2004 Congress on Evolutionary Computation (CEC 2004), IEEE Press, Piscataway, NJ, USA

    Google Scholar 

  • Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1992) Numerical recipes in C. Cambridge University Press

    Google Scholar 

  • Price KV (1997) Differential evolution vs. the contest functions of the 2nd ICEO. In: Proceedings of the 1997 IEEE international conference on evolutionary computation, April 13–16, Indianapolis, IN, USA. IEEE Press, pp 153–157

    Google Scholar 

  • Price WL (1977) Global optimization by controlled random search. Computer Journal 20:367–370

    Article  MATH  Google Scholar 

  • Plagianakos VP, Magoulas GD, Nousis NK, Vrahatis MN (2001) Training multilayer networks with discrete activation functions. In: Proceedings of the INNS-IEEE international joint conference on neural networks, July 14–19, 2001, Washington DC, USA

    Google Scholar 

  • Rogalsky T, Derksen RW, Kocabiyik S (1999) Differential evolution in aerodynamic optimization. In: Proceedings of the 46th annual conference of the Canadian aeronautics and space institute, May 2–5, 1999, pp 29–36. Available via Internet: http://home.cc.umanitoba.ca/~umrogal1/publications.html

    Google Scholar 

  • Růžek B, Kvasnička M (2001) Differential evolution algorithm in the earthquake hypocenter location. Pure and Applied Geophysics 158:667–693

    Article  Google Scholar 

  • Salomon R (1996) Reevaluating 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

    Article  Google Scholar 

  • Sandgren E (1990) Nonlinear integer and discrete programming in mechanical design optimization. Transactions of the ASME, Journal of Mechanical Design 112(2):223–229

    Article  Google Scholar 

  • Schwefel H-P (1995) Evolution and optimum seeking. Wiley

    Google Scholar 

  • Stanhope SA, Daida JM (1997) An individually variable mutation rate strategy for genetic algorithms. In: Angeline PJ, Reynolds RJ, McDonnell JR, Eberhart R (eds) Evolutionary programming VI; Lecture notes in computer science 1213. Springer, pp 235–245

    Google Scholar 

  • 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

    Article  MathSciNet  MATH  Google Scholar 

  • Thierauf G, Cai J (1997) Evolution strategies — parallelization and application in engineering optimization. In: Topping BHV (ed) Parallel and distributed processing for computational mechanics. Saxe-Coburg Publications, Edinburgh

    Google Scholar 

  • Thomas P, Vernon D (1997) Image registration by differential evolution. In: Proceedings of the first Irish machine vision and image processing conference IMVIP-97, Magee College, University of Ulster, pp 221–225. PostScript file available from http://www.cs.may.ie/~pthomas/

    Google Scholar 

  • Thomsen R (2003) Flexible ligand docking using evolutionary algorithms: Investigating the effects of variation operators and local search hybrids. Biosystems 72(1–2):57–73

    Article  Google Scholar 

  • Tom DJ (1990) Training binary node feed forward neural networks by backpropagation of error. Electronics Letters 26:1745–1746

    Google Scholar 

  • Tsai J-T, Liu T-K, Chou J-H (2004) Hybrid Taguchi-genetic algorithm for global numerical optimization. IEEE Transactions on Evolutionary Computation 8(4):365–377

    Article  Google Scholar 

  • Tu Z, Lu Y (2004) A robust stochastic genetic algorithm for global numerical optimization. IEEE Transactions on Evolutionary Computation 8(5):456–470

    Article  Google Scholar 

  • Ursem RK, Vadstrup P (2004) Parameter identification of induction motors using differential evolution. Applied Soft Computing 4(1): 49–64

    Article  Google Scholar 

  • Van den Bergh F, Englebrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation 8(3):225–239

    Article  Google Scholar 

  • Vesterstrøm JS, Riget J (2002) Particle swarms: Extensions for improved local, multi-modal and dynamic search in numerical optimization. Master’s thesis, EVALife, Dept. of Computer Science, University of Aarhus, Denmark

    Google Scholar 

  • Vesterstrøm J, Thomsen R (2004) A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Proceedings of the 2004 congress on evolutionary computing, vol 2, pp 1980–1987

    Article  Google Scholar 

  • Voigt H-M (1995) Soft genetic operators in evolutionary computation and biocomputation. In: Lecture Notes in Computer Science 899. Springer, Berlin, pp 123–141

    Google Scholar 

  • Whitley D, Mathias K, Rana S, Dzubera J (1996) Evaluating evolutionary algorithms. Artificial Intelligence 85:1–32

    Article  Google Scholar 

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE transactions on evolutionary computation, IEEE Press, 1(1):67–82

    Article  Google Scholar 

  • Wu S-J, Chow P-T (1995) Genetic algorithms for nonlinear mixed discreteinterger optimization problems via meta-genetic parameter optimization. Engineering Optimization 24(2): 137–159

    Google Scholar 

  • Yao X, Liu Y (1997) Fast Evolution Strategies. In: Angeline PJ, Reynolds RJ, McDonnell JR, Eberhart R (eds) Evolutionary programming VI. Springer, Berlin, pp 151–161

    Google Scholar 

  • Yao X, Liu Y, Lin GM (1999) Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3:82–102

    Article  Google Scholar 

  • Yen J, Lee B (1997) A simplex genetic algorithm hybrid. In: Proceedings of the 1997 IEEE conference on evolutionary computation, Indianapolis, Indiana, April 13–16. IEEE Press, pp 175–180

    Google Scholar 

  • Zimmermann W (1990) Operations research. Oldenbourg

    Google Scholar 

  • Zitzler E, Thiele I (1999) Multi-objective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 4:257–271

    Article  Google Scholar 

  • Zitzler E, Deb K, Thiele L (2000) Comparison of multi-objective evolutionary algorithms: Empirical results. Evolutionary Computation 8:173–195

    Article  Google Scholar 

Download references

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

(2005). Benchmarking Differential Evolution. In: Differential Evolution. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31306-0_3

Download citation

  • DOI: https://doi.org/10.1007/3-540-31306-0_3

  • 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)

Publish with us

Policies and ethics