Skip to main content
Springer Nature Link
Log in

Evolutionary Optimization

  • Chapter
Variants of Evolutionary Algorithms for Real-World Applications

Abstract

The emergence of different metaheuristics and their new variants in recent years has made the definition of the term Evolutionary Algorithms unclear. Originally, it was coined to put a group of stochastic search algorithms that mimic natural evolution together. While some people would still see it as a specific term devoted to this group of algorithms, including Genetic Algorithms, Genetic Programming, Evolution Strategies, Evolutionary Programming, and to a lesser extent Differential Evolution and Estimation of Distribution Algorithms, many others would regard “Evolutionary Algorithms” as a general term describing population-based search methods that involve some form of randomness and selection. In this chapter, we re-visit the fundamental question of “what is an Evolutionary Algorithm?” not only from the traditional viewpoint but also the wider, more modern perspectives relating it to other areas of Evolutionary Computation. To do so, apart from discussing the main characteristics of this family of algorithms we also look at Memetic Algorithms and the Swarm Intelligence algorithms. From our discussion, we see that establishing semantic borders between these algorithm families is not always easy, nor necessarily useful. It is anticipated that they will further converge as the research from these areas cross-fertilizes each other.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Bäck, T., Hoffmeister, F., Schwefel, H.: A Survey of Evolution Strategies. In: Belew, R.K., Booker, L.B. (eds.) Proceedings of the Fourth International Conference on Genetic Algorithms (ICGA 1991), pp. 2–9. Morgan Kaufmann Publishers Inc., San Francisco (1991)

    Google Scholar 

  2. Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. Oxford University Press, Inc., USA (1997)

    MATH  Google Scholar 

  3. Baluja, S., Caruana, R.A.: Removing the Genetics from the Standard Genetic Algorithm. In: Prieditis, A., Russell, S.J. (eds.) Proceedings of the Twelfth International Conference on Machine Learning (ICML 1995), pp. 38–46. Morgan Kaufmann Publishers Inc., San Francisco (1995)

    Google Scholar 

  4. Barricelli, N.A.: Esempi Numerici di Processi di Evoluzione. Methodos 6(21-22), 45–68 (1954)

    MathSciNet  Google Scholar 

  5. Barricelli, N.A.: Symbiogenetic Evolution Processes Realized by Artificial Methods. Methodos 9(35-36), 143–182 (1957)

    Google Scholar 

  6. Belew, R.K., Booker, L.B. (eds.): Proceedings of the Fourth International Conference on Genetic Algorithms (ICGA 1991), July13–16, pp. 13–16. Morgan Kaufmann Publishers Inc., USA (1991)

    Google Scholar 

  7. Beyer, H.: The Theory of Evolution Strategies, Natural Computing Series, Springer, New York (May 27, 2001); ISBN: 3-540-67297-4

    Google Scholar 

  8. Beyer, H., Schwefel, H.: Evolution Strategies – A Comprehensive Introduction. Natural Computing: An International Journal 1(1), 3–52 (2002); doi10.1023/A:1015059928466

    Article  MathSciNet  MATH  Google Scholar 

  9. Blum, C.: Ant Colony Optimization: Introduction and Recent Trends. Physics of Life Reviews 2(4), 353–373 (2005); doi:10.1016/j.plrev.2005.10.001

    Article  Google Scholar 

  10. Blum, C., Dorigo, M.: The Hyper-Cube Framework for Ant Colony Optimization. IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics 34(2), 1161–1172 (2004); doi:10.1109/TSMCB.2003.821450

    Article  Google Scholar 

  11. Blum, C., Roli, A.: Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison. ACM Computing Surveys (CSUR) 35(3), 268–308 (2003); doi:10.1145/937503.937505

    Article  Google Scholar 

  12. Bonabeau, E.W., Dorigo, M., Théraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, Inc., USA (1999); ISBN: 0195131592

    MATH  Google Scholar 

  13. Bonabeau, E.W., Dorigo, M., Théraulaz, G.: Inspiration for Optimization from Social Insect Behavior. Nature 406, 39–42 (2000); doi:10.1038/35017500

    Article  Google Scholar 

  14. Burke, E.K., Kendall, G., Soubeiga, E.: A Tabu Search Hyperheuristic for Timetabling and Rostering. Journal of Heuristics 9(6), 451–470 (2003); doi:10.1023/B:HEUR.0000012446.94732.b6

    Article  Google Scholar 

  15. Campos, M., Bonabeau, E.W., Théraulaz, G., Deneubourg, J.: Dynamic Scheduling and Division of Labor in Social Insects. Adaptive Behavior 8(2), 83–95 (2000); doi:10.1177/105971230000800201

    Article  Google Scholar 

  16. Caponio, A., Cascella, G.L., Neri, F., Salvatore, N., Sumner, M.: A Fast Adaptive Memetic Algorithm for Online and Offline Control Design of PMSM Drives. IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics 37(1), 28–41 (2007); doi:10.1109/TSMCB.2006.883271

    Article  Google Scholar 

  17. 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(8-9), 811–831 (2009); doi:10.1007/s00500-008-0357-1

    Google Scholar 

  18. Chen, W.X., Weise, T., Yang, Z.Y., Tang, K.: Large-Scale Global Optimization Using Cooperative Coevolution with Variable Interaction Learning. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G., et al. (eds.) PPSN XI. LNCS, vol. 6239, pp. 300–309. Springer, Heidelberg (2010), doi:10.1007/978-3-642-15871-1-31

    Chapter  Google Scholar 

  19. Chiong, R. (ed.): Nature-Inspired Algorithms for Optimisation, April 30. SCI, vol. 193. Springer, Heidelberg (2009); ISBN: 3-642-00266-8, 3-642-00267-6, doi:10.1007/978-3-642-00267-0

    Google Scholar 

  20. Chiong, R., Weise, T., Michalewicz, Z. (eds.): Variants of Evolutionary Algorithms for Real-World Applications. Springer, Heidelberg (2011)

    Google Scholar 

  21. Clerc, M., Kennedy, J.: The Particle Swarm – Explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002); doi:10.1109/4235.985692

    Article  Google Scholar 

  22. Coello Coello, C.A., Lamont, G.B., van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems. Genetic and Evolutionary Computation, vol. 5. Springer, Heidelberg (2002); doi:10.1007/978-0-387-36797-2

    MATH  Google Scholar 

  23. Cowling, P., Kendall, G., Soubeiga, E.: A Hyperheuristic Approach to Scheduling a Sales Summit. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 176–190. Springer, Heidelberg (2001); doi:10.1007/3-540-44629-X-11

    Chapter  Google Scholar 

  24. Dawkins, R.: The Selfish Gene, 1st, 2nd edn. Oxford University Press, Inc., USA (1976); ISBN:0-192-86092-5

    Google Scholar 

  25. De Jong, K.A.: An Analysis of the Behavior of a Class of Genetic Adaptive Systems, PhD thesis, University of Michigan: Ann Arbor, MI, USA (1975)

    Google Scholar 

  26. De Jong, K.A.: Genetic Algorithms are NOT Function Optimizers. In: Whitley, L.D. (ed.) Proceedings of the Second Workshop on Foundations of Genetic Algorithms (FOGA 1992), pp. 5–17. Morgan Kaufmann Publishers Inc., San Francisco (1992)

    Google Scholar 

  27. K. A. De Jong. Evolutionary Computation: A Unified Approach, 2006, volume 4 of Complex Adaptive Systems. MIT Press: Cambridge, MA, USA.

    Google Scholar 

  28. Deb, K., Goldberg, D.E.: Analyzing Deception in Trap Functions. In: Whitley, L.D. (ed.) Proceedings of the Second Workshop on Foundations of Genetic Algorithms (FOGA 1992), pp. 93–108. Morgan Kaufmann Publishers Inc., San Francisco (1992)

    Google Scholar 

  29. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley Interscience Series in Systems and Optimization, John Wiley & Sons Ltd., New York (2001)

    Google Scholar 

  30. Deb, K., Pratab, A., Agrawal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002); doi:10.1109/4235.996017

    Article  Google Scholar 

  31. Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics 26(1), 29–41 (1996); doi:10.1109/3477.484436, ftp://iridia.ulb.ac.be/pub/mdorigo/journals/IJ.10-SMC96.pdf

    Article  Google Scholar 

  32. Dorigo, M., Stützle, T.: Ant Colony Optimization. Bradford Books. MIT Press (July 2004); ISBN: 0-262-04219-3

    Google Scholar 

  33. Eberhart, R.C., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science (MHS 1995), pp. 39–43. IEEE Computer Society, USA (1995); doi:10.1109/MHS.1995.494215

    Chapter  Google Scholar 

  34. Eberhart, R.C., Shi, Y.: A Modified Particle Swarm Optimizer. In: Simpson, P.K. (ed.) The 1998 IEEE International Conference on Evolutionary Computation (CEC 1998), pp. 69–73. IEEE Computer Society, Los Alamitos (1998); doi:10.1109/ICEC.1998.699146

    Google Scholar 

  35. Eiben, Á.E., Smith, J.E.: Introduction to Evolutionary Computing, 1st edn. Natural Computing Series, ch. 10, pp. 173–188. Springer, New York (2003)

    MATH  Google Scholar 

  36. Farooq, M.: Bee-Inspired Protocol Engineering – From Nature to Networks. Natural Computing Series, vol. 15. Springer, New York (2009); ISBN: 3-540-85953-5, doi:10.1007/978-3-540-85954-3

    Google Scholar 

  37. Fogel, L.J.: On the Organization of Intellect. PhD thesis, University of California (UCLA): Los Angeles, CA, USA (1964)

    Google Scholar 

  38. Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence through Simulated Evolution. John Wiley & Sons Ltd., USA (1966); ISBN: 0471265160

    MATH  Google Scholar 

  39. Fraser, A.S.: Simulation of Genetic Systems by Automatic Digital Computers. I. Introduction. Australian Journal of Biological Science (AJBS) 10, 484–491 (1957)

    Google Scholar 

  40. Gao, Y., Duan, Y.: An Adaptive Particle Swarm Optimization Algorithm with New Random Inertia Weight. In: Huang, D.-S., Heutte, L., Loog, M. (eds.) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques - ICIC 2007. LNCS, vol. 2, pp. 342–350. Springer, Heidelberg (2007), doi:10.1007/978-3-540-74282-1-39

    Chapter  Google Scholar 

  41. Gao, Y., Ren, Z.: Adaptive Particle Swarm Optimization Algorithm With Genetic Mutation Operation. In: Lei, J., Yao, J., Zhang, Q. (eds.) Proceedings of the Third International Conference on Advances in Natural Computation (ICNC’07), vol. 2, pp. 211–215. IEEE Computer Society Press, Los Alamitos (2007), doi:10.1109/ICNC.2007.161

    Chapter  Google Scholar 

  42. Glover, F., Kochenberger, G.A. (eds.): Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 57. Kluwer, Springer Netherlands, Dordrecht, Netherlands (2003), doi:10.1007/b101874

    Google Scholar 

  43. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Longman Publishing Co, USA (1989); ISBN: 0-201-15767-5

    MATH  Google Scholar 

  44. Gong, M., Jiao, L., Zhang, L.: Baldwinian Learning in Clonal Selection Algorithm for Optimization. Information Sciences – Informatics and Computer Science Intelligent Systems Applications: An International Journal 180(8), 1218–1236 (2010); doi:10.1016/j.ins.2009.12.007

    Google Scholar 

  45. Gonzalez, T.F. (ed.): Handbook of Approximation Algorithms and Metaheuristics, Chapmann & Hall/CRC Computer and Information Science Series. Chapman & Hall/CRC, Boca Raton, FL (2007)

    Google Scholar 

  46. Grefenstette, J.J.: Deception Considered Harmful. In: Whitley, L.D. (ed.) Proceedings of the Second Workshop on Foundations of Genetic Algorithms (FOGA 1992), pp. 75–91. Morgan Kaufmann Publishers Inc., USA (1992)

    Google Scholar 

  47. Hansen, N., Ostermeier, A.: Adapting Arbitrary Normal Mutation Distributions in Evolution Strategies: The Covariance Matrix Adaptation. In: Jidō, K., Gakkai, S. (eds.) Proceedings of IEEE International Conference on Evolutionary Computation (CEC 1996), pp. 312–317. IEEE Computer Society Press, Los Alamitos (1996); doi:10.1109/ICEC.1996.542381

    Chapter  Google Scholar 

  48. Hansen, N., Ostermeier, A.: Convergence Properties of Evolution Strategies with the Derandomized Covariance Matrix Adaption: The (μ/μ I ,λ)-CMA-ES. In: Zimmermann, H. (ed.) Proceedings of the 5th European Congress on Intelligent Techniques and Soft Computing (EUFIT 1997), vol. 1, pp. 650–654. ELITE Foundation, Germany (1997)

    Google Scholar 

  49. Hansen, N., Ostermeier, A.: Completely Derandomized Self-Adaptation in Evolution Strategies. Evolutionary Computation 9(2), 159–195 (2001)

    Article  Google Scholar 

  50. Hansen, N., Ostermeier, A., Gawelczyk, A.: On the Adaptation of Arbitrary Normal Mutation Distributions in Evolution Strategies: The Generating Set Adaptation. In: Eshelman, L.J. (ed.) Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA 1995), pp. 57–64. Morgan Kaufmann Publishers Inc., San Francisco (1995)

    Google Scholar 

  51. Hart, W.E., Krasnogor, N., Smith, J.E.: Memetic Evolutionary Algorithms. In: Hart, W.E., Krasnogor, N., Smith, J.E. (eds.) Recent Advances in Memetic Algorithms. Studies in Fuzziness and Soft Computing, ch.1, vol. 166, pp. 3–27. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  52. Grefenstette, J.J.: Proceedings of the 1st International Conference on Genetic Algorithms and their Applications (ICGA 1985), June 24-26, pp. 24–26. Carnegy Mellon University (CMU), Lawrence Erlbaum Associates, Hillsdale, USA (1985)

    Google Scholar 

  53. Hillis, W.D.: Co-Evolving Parasites Improve Simulated Evolution as an Optimization Procedure. Physica D: Nonlinear Phenomena 42(1-2), 228–234 (1990); doi:10.1016/0167-2789(90)90076-2

    Article  Google Scholar 

  54. Hitch-Hiker’s Guide to Evolutionary Computation: A List of Frequently Asked Questions (FAQ) (HHGT) (March 29, 2000)

    Google Scholar 

  55. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, USA (1975); ISBN: 0-472-08460-7

    Google Scholar 

  56. Holland, J.H.: Genetic Algorithms. Scientific American 267(1), 44–50 (1992)

    Article  Google Scholar 

  57. Igel, C., Toussaint, M.: On Classes of Functions for which No Free Lunch Results Hold. Information Processing Letters 86(6), 317–321 (2003); doi:10.1016/S0020-0190(03)00222-9

    Article  MathSciNet  MATH  Google Scholar 

  58. Jastrebski, G.A., Arnold, D.V.: Improving Evolution Strategies through Active Covariance Matrix Adaptation. In: Yen, G.G., et al. (eds.) Proceedings of the IEEE Congress on Evolutionary Computation CEC 2006, pp. 9719–9726. IEEE Computer Society, Los Alamitos (2006); doi:10.1109/CEC.2006.1688662

    Google Scholar 

  59. Kendall, G., Cowling, P., Soubeiga, E.: Choice Function and Random HyperHeuristics. In: Tan, K.C., et al. (eds.) Recend Advances in Simulated Evolution and Learning – Proceedings of the Fourth Asia-Pacific Conference on Simulated Evolution And Learning (SEAL 2002). Advances in Natural Computation, vol. 2, pp. 667–671. World Scientific Publishing Co, Singapore (2002)

    Google Scholar 

  60. Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks (ICNN 1995), vol. 4, pp. 1942–1948. IEEE Computer Society Press, Los Alamitos (1995); doi:10.1109/ICNN.1995.488968

    Chapter  Google Scholar 

  61. Kononova, A.V., Ingham, D.B., Pourkashanian, M.: Simple Scheduled Memetic Algorithm for Inverse Problems in Higher Dimensions: Application to Chemical Kinetics. In: Michalewicz, Z., Reynolds, R.G. (eds.) Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2008), pp. 3905–3912. IEEE Computer Society Press, Los Alamitos (2008); doi:10.1109/CEC.2008.4631328

    Google Scholar 

  62. Korošec, P., Šilc, J., Filipič, B.: The Differential Ant-Stigmergy Algorithm. Information Sciences – Informatics and Computer Science Intelligent Systems Applications: An International Journal (2011)

    Google Scholar 

  63. Koza, J.R.: Concept Formation and Decision Tree Induction using the Genetic Programming Paradigm. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 124–128. Springer, Heidelberg (1991); doi:10.1007/BFb0029742

    Chapter  Google Scholar 

  64. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, 1st edn. Bradford Books, MIT Press (1992); 2nd edn. (1993)

    Google Scholar 

  65. Koza, J.R., Andre, D., Bennett III, F.H., Keane, M.A.: Use of Automatically Defined Functions and Architecture-Altering Operations in Automated Circuit Synthesis Using Genetic Programming. In: Koza, J.R., et al. (eds.) Proceedings of the First Annual Conference of Genetic Programming (GP 1996), Complex Adaptive Systems, Bradford Books, pp. 132–149. MIT Press, Cambridge (1996)

    Google Scholar 

  66. Kramer, O.: Self-Adaptive Heuristics for Evolutionary Computation. SCI, vol. 147. Springer, Heidelberg (2008); doi:10.1007/978-3-540-69281-2

    MATH  Google Scholar 

  67. Krasnogor, N., Smith, J.E.: A Tutorial for Competent Memetic Algorithms: Model, Taxonomy, and Design Issues. IEEE Transactions on Evolutionary Computation 9(5), 474–488 (2005); doi:10.1109/TEVC.2005.850260

    Article  Google Scholar 

  68. Larrañaga, P., Lozano, J.A. (eds.): Estimation of Distribution Algorithms – A New Tool for Evolutionary Computation. Genetic and Evolutionary Computation, vol. 2. Springer US, USA (2001)

    Google Scholar 

  69. Le, M.N., Ong, Y., Jin, Y., Sendhoff, B.: Lamarckian Memetic Algorithms: Local Optimum and Connectivity Structure Analysis. Memetic Computing 1(3), 175–190 (2009); doi:10.1007/s12293-009-0016-9

    Article  Google Scholar 

  70. Mendes, R., Kennedy, J., Neves, J.: Fully Informed Particle Swarm: Simpler, Maybe Better. IEEE Transactions on Evolutionary Computation 8(3), 204–210 (2004); doi:10.1109/TEVC.2004.826074

    Article  Google Scholar 

  71. Mendes, R.R.F., de Voznika, F.B., Freitas, A.A., Nievola, J.C.: Discovering Fuzzy Classification Rules with Genetic Programming and Co-evolution. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 314–325. Springer, Heidelberg (2001), doi:10.1007/3-540-44794-6-26

    Chapter  Google Scholar 

  72. Meyer-Nieberg, S., Beyer, H.: Self-Adaptation in Evolutionary Algorithms. In: Lobo, F.G., Lima, C.F., Michalewicz, Z. (eds.) Parameter Setting in Evolutionary Algorithms. SCI, ch. 3, vol. 54, pp. 47–75. Springer, Heidelberg (2007), doi:10.1007/978-3-540-69432-8-3

    Chapter  Google Scholar 

  73. Michalewicz, Z.: A Perspective on Evolutionary Computation. In: Yao, X. (ed.) AI-WS 1993 and 1994. LNCS, vol. 956, pp. 73–89. Springer, Heidelberg (1995), doi:10.1007/3-540-60154-6-49

    Google Scholar 

  74. Michalewicz, Z., Schaffer, J.D., Schwefel, H.-P., Fogel, D.B., Kitano, H.: Proceedings of the First IEEE Conference on Evolutionary Computation (CEC 1994), June 27-29, pp. 27–29. IEEE Computer Society Press, Los Alamitos (1997)

    Google Scholar 

  75. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1996)

    MATH  Google Scholar 

  76. Michalewicz, Z., Fogel, D.B.: How to Solve It: Modern Heuristics, 2nd extended edn. Springer, Heidelberg (2004)

    MATH  Google Scholar 

  77. Moscato, P.: On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Caltech Concurrent Computation Program C3P 826, California Institute of Technology (Caltech), Caltech Concurrent Computation Program (C3P), Pasadena (1989)

    Google Scholar 

  78. Neri, F., Tirronen, V., Kärkkäinen, T., Rossi, T.: Fitness Diversity based Adaptation in Multimeme Algorithms: A Comparative Study. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2007), pp. 2374–2381. IEEE Computer Society, Los Alamitos (2007); doi:10.1109/CEC.2007.4424768

    Chapter  Google Scholar 

  79. Neri, F., Toivanen, J., Cascella, G.L., Ong, Y.: An Adaptive Multimeme Algorithm for Designing HIV Multidrug Therapies. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) 4(2) (April 2007); doi:10.1109/TCBB.2007.070202

    Google Scholar 

  80. 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 – The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies 27(3), 219–235 (2007); doi:10.1007/s10489-007-0069-8

    Google Scholar 

  81. Nguyen, Q.H., Ong, Y., Lim, M.H., Krasnogor, N.: Adaptive Cellular Memetic Algorithms. Evolutionary Computation 17(2), 231–256 (2009); doi:10.1162/evco.2009.17.2.231

    Article  Google Scholar 

  82. Norman, M.G., Moscato, P.: A Competitive and Cooperative Approach to Complex Combinatorial Search. Caltech Concurrent Computation Program 790, California Institute of Technology (Caltech), Caltech Concurrent Computation Program (C3P), Pasadena (1989)

    Google Scholar 

  83. Norman, M.G., Moscato, P.: A Competitive and Cooperative Approach to Complex Combinatorial Search. In: Proceedings of the 20th Informatics and Operations Research Meeting (20th Jornadas Argentinas e Informática e Investigación Operativa) (JAIIO 1991), pp. 3.15–3.29 (1991); Also published as Technical Report Caltech Concurrent Computation Program, Report. 790, California Institute of Technology, Pasadena, California, USA (1989)

    Google Scholar 

  84. Ong, Y., Keane, A.J.: Meta-Lamarckian Learning in Memetic Algorithms. IEEE Transactions on Evolutionary Computation 8(2), 99–110 (2004); doi:10.1109/TEVC.2003.819944

    Article  Google Scholar 

  85. Ong, Y., Lim, M.H., Zhu, N., Wong, K.: Classification of Adaptive Memetic Algorithms: A Comparative Study. IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics 36(1), 141–152 (2006); doi:10.1109/TSMCB.2005.856143

    Article  Google Scholar 

  86. Parrish, J.K., Hamner, W.M. (eds.): Animal Groups in Three Dimensions: How Species Aggregate. Cambridge University Press, Cambridge (1997); doi:10.2277/0521460247, ISBN: 0521460247

    Google Scholar 

  87. Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming. Lulu Enterprises UK Ltd., UK (2008)

    Google Scholar 

  88. Potter, M.A., De Jong, K.A.: A Cooperative Coevolutionary Approach to Function Optimization. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994); doi:10.1007/3-540-58484-6-269

    Google Scholar 

  89. Potter, M.A., De Jong, K.A.: Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents. Evolutionary Computation 8(1), 1–29 (2000)

    Article  Google Scholar 

  90. Rechenberg, I.: Cybernetic Solution Path of an Experimental Problem. Royal Aircraft Establishment, Farnborough (1965)

    Google Scholar 

  91. Rechenberg, I.: Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. PhD thesis, Technische Universität Berlin: Berlin, Germany (1971)

    Google Scholar 

  92. Rechenberg, I.: Evolutionsstrategie 1994. Werkstatt Bionik und Evolutionstechnik, vol. 1. Frommann-Holzboog Verlag, Germany (1994)

    Google Scholar 

  93. Schwefel, H.: Kybernetische Evolution als Strategie der exprimentellen Forschung in der Strömungstechnik. Master’s thesis, Technische Universität Berlin: Berlin, Germany (1965)

    Google Scholar 

  94. Schwefel, H.: Experimentelle Optimierung einer Zweiphasendüse Teil I. Technical Report 35, AEG Research Institute: Berlin, Germany, Project MHD–Staustrahlrohr 11.034/68 (1968)

    Google Scholar 

  95. Schwefel, H.: Evolutionsstrategie und numerische Optimierung. PhD thesis, Technische Universität Berlin, Institut für Meß- und Regelungstechnik, Institut für Biologie und Anthropologie: Berlin, Germany (1975)

    Google Scholar 

  96. Schwefel, H.: Evolution and Optimum Seeking. Sixth-Generation Computer Technology Series. John Wiley & Sons Ltd., USA (1995); ISBN: 0-471-57148-2

    Google Scholar 

  97. Smith, J.E.: Coevolving Memetic Algorithms: A Review and Progress Report. IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics 37(1), 6–17 (2007); doi:10.1109/TSMCB.2006.883273

    Article  Google Scholar 

  98. Srinivas, N., Deb, K.: Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation 2(3), 221–248 (1994); doi:10.1162/evco.1994.2.3.221

    Article  Google Scholar 

  99. Tang, J., Lim, M.H., Ong, Y.: Diversity-Adaptive Parallel Memetic Algorithm for Solving Large Scale Combinatorial Optimization Problems. Soft Computing – A Fusion of Foundations, Methodologies and Applications 11(9), 873–888 (2007); doi:10.1007/s00500-006-0139-6

    Google Scholar 

  100. 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); doi:10.1162/evco.2008.16.4.529

    Article  Google Scholar 

  101. Wang, P., Weise, T., Chiong, R.: Novel Evolutionary Algorithms for Supervised Classification Problems: An Experimental Study. Evolutionary Intelligence 4(1), 3–16 (2011); doi:10.1007/s12065-010-0047-7

    Article  Google Scholar 

  102. Weise, T.: Global Optimization Algorithms – Theory and Application. it-weise.de (self-published), Germany (2009), http://www.it-weise.de/projects/book.pdf

  103. Weise, T., Tang, K.: Evolving Distributed Algorithms with Genetic Programming. IEEE Transactions on Evolutionary Computation (to appear, 2011)

    Google Scholar 

  104. Weise, T., Podlich, A., Gorldt, C.: Solving Real-World Vehicle Routing Problems with Evolutionary Algorithms. In: Chiong, R., Dhakal, S. (eds.) Natural Intelligence for Scheduling, Planning and Packing Problems. SCI, ch.2, vol. 250, pp. 29–53. Springer, Heidelberg (2009), doi:10.1007/978-3-642-04039-9-2

    Chapter  Google Scholar 

  105. Weise, T., Podlich, A., Reinhard, K., Gorldt, C., Geihs, K.: Evolutionary Freight Transportation Planning. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., Machado, P. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 768–777. Springer, Heidelberg (2009), doi:10.1007/978-3-642-01129-0-87

    Chapter  Google Scholar 

  106. Weise, T., Zapf, M., Chiong, R., Nebro Urbaneja, A.J.: Why Is Optimization Difficult? In: Chiong, R. (ed.) Nature-Inspired Algorithms for Optimisation. SCI,  ch. 1, vol. 193, pp. 1–50. Springer, Heidelberg (2009); doi:10.1007/978-3-642-00267-0-1

    Chapter  Google Scholar 

  107. Weise, T., Niemczyk, S., Chiong, R., Wan, M.: A Framework for Multi-Model EDAs with Model Recombination. In: Proceedings of the 4th European Event on Bio-Inspired Algorithms for Continuous Parameter Optimisation (EvoNUM 2011), Proceedings of the European Conference on the Applications of Evolutionary Computation (EvoAPPLICATIONS 2011). LNCS, Springer, Heidelberg (2011)

    Google Scholar 

  108. Whitley, L.D.: A Genetic Algorithm Tutorial. Statistics and Computing 4(2), 65–85 (1994); doi:10.1007/BF00175354

    Article  Google Scholar 

  109. Wolpert, D.H., Macready, W.G.: No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation 1(1), 67–82 (1997); doi:10.1109/4235.585893

    Article  Google Scholar 

  110. Yao, X., Liu, Y., Lin, G.: Evolutionary Programming Made Faster. IEEE Transactions on Evolutionary Computation 3(2), 82–102 (1999); doi:10.1109/4235.771163

    Article  Google Scholar 

  111. Yu, E.L., Suganthan, P.N.: Ensemble of Niching Algorithms. Information Sciences – Informatics and Computer Science Intelligent Systems Applications: An International Journal 180(15), 2815–2833 (2010); doi:10.1016/j.ins.2010.04.008

    MathSciNet  Google Scholar 

  112. Yuan, Q., Qian, F., Du, W.: A Hybrid Genetic Algorithm with the Baldwin Effect. Information Sciences – Informatics and Computer Science Intelligent Systems Applications: An International Journal 180(5), 640–652 (2010), doi:10.1016/j.ins.2009.11.015

    MathSciNet  MATH  Google Scholar 

  113. Zitzler, E., Thiele, L.: An Evolutionary Algorithm for Multiobjective Optimization: The Strength Pareto Approach. 43, Eidgenssische Technische Hochschule (ETH) Zürich, Department of Electrical Engineering, Computer Engineering and Networks Laboratory (TIK), Zürich, Switzerland (May 1995)

    Google Scholar 

  114. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. 101, Eidgenssische Technische Hochschule (ETH) Zürich, Department of Electrical Engineering, Computer Engineering and Networks Laboratory (TIK), Zürich, Switzerland (May 2001)

    Google Scholar 

  115. Zlochin, M., Birattari, M., Meuleau, N., Dorigo, M.: Model-Based Search for Combinatorial Optimization: A Critical Survey. Annals of Operations Research 132(1-4), 373–395 (2004), doi:10.1023/B:ANOR.0000039526.52305.af

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ALBCOM Research Group, Universitat Politècnica de Catalunya, Barcelona, Spain

    Christian Blum

  2. Faculty of Information & Communication Technologies, Swinburne University of Technology, Victoria, 3122, Australia

    Raymond Chiong

  3. Independent Consultant, Groisy, France

    Maurice Clerc

  4. Department of Computer Science, George Mason University, Fairfax, VA, 22030, USA

    Kenneth De Jong

  5. School of Computer Science, University of Adelaide, South Australia, 5005, Australia

    Zbigniew Michalewicz

  6. Institute of Computer Science, Polish Academy of Sciences, ul. Ordona 21, 01-237, Warsaw, Poland

    Zbigniew Michalewicz

  7. Polish-Japanese Institute of Information Technology, ul. Koszykowa 86, 02-008, Warsaw, Poland

    Zbigniew Michalewicz

  8. Department of Mathematical Information Technology, University of Jyväskylä, P. O. Box 35, Agora, 40014, Finland

    Ferrante Neri

  9. Nature Inspired Computation and Applications Laboratory (NICAL), School of Computer Science and Technology, University of Science and Technology of China (USTC), Héféi, 230027, Ānhuī, China

    Thomas Weise

Authors
  1. Christian Blum
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raymond Chiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maurice Clerc
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kenneth De Jong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zbigniew Michalewicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ferrante Neri
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Thomas Weise
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of ICT, Swinburne University of Technology, 3122, Melbourne, VIC, Australia

    Raymond Chiong

  2. Nature Inspired Computation and Applications Laboratory School of Computer Science and Technology, University of Science and Technology of China (USTC), 230027, Hefei, Anhui, China

    Thomas Weise

  3. School of Computer Science, University of Adelaide, 5005, Adelaide, SA, Australia

    Zbigniew Michalewicz

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Blum, C. et al. (2012). Evolutionary Optimization. In: Chiong, R., Weise, T., Michalewicz, Z. (eds) Variants of Evolutionary Algorithms for Real-World Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23424-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-23424-8_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23423-1

  • Online ISBN: 978-3-642-23424-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics