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Next Generation Genetic Algorithms: A User’s Guide and Tutorial

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Handbook of Metaheuristics

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 272))

Abstract

Genetic algorithms are different from most other metaheuristics because they exploit three key ideas: (1) the use of a population of solutions to guide search, (2) the use of crossover operators that recombine two or more solutions to generate new and potentially better solutions, and (3) the active management of diversity to sustain exploration. New ideas that are also introduced in this chapter include (1) the use of deterministic recombination operators that are capable of tunneling between local optima, and (2) the use of deterministic constant time move operators.

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References

  1. E. Alba, G. Lugue, Parallel Genetic Algorithms: Theory and Real World Applications, vol. 367 (Springer, Berlin, 2011)

    Google Scholar 

  2. Th. Bäck, C. Foussette, P. Krause, Contemporary Evolutionary Strategies (Springer, Berlin, 2013)

    Google Scholar 

  3. E. Boros, P.L. Hammer, Pseudo-boolean optimization. Discret. Appl. Math. 123(1), 155–225 (2002)

    Google Scholar 

  4. F. Chicano, D. Whitley, A. Sutton, Efficient identification of improving moves in a ball for pseudo-boolean problems, in Genetic and Evolutionary Computation Conference (GECCO) (ACM, New York, 2014), pp. 437–444

    Google Scholar 

  5. W. Cook, Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation (Princeton University Press, Princeton, 2011)

    Google Scholar 

  6. G.A. Croes, A method for solving traveling salesman problems. Oper. Res. 6(6), 791–812 (1958)

    Google Scholar 

  7. L. Davis, Handbook of Genetic Algorithms (Van Nostrand Reinhold, New York, 1991)

    Google Scholar 

  8. K. Deb, C. Myburgh, Breaking the billion variable barrier in real world optimization, in Genetic and Evolutionary Computation Conference (GECCO) (ACM, New York, 2016), pp. 653–660

    Google Scholar 

  9. K. DeJong, An analysis of the behavior of a class of genetic adaptive systems. Ph.D. thesis, University of Michigan, Department of Computer and Communication Sciences, Ann Arbor, 1975

    Google Scholar 

  10. K. DeJong, Genetic algorithms are NOT function optimizers, in Foundations of Genetic Algorithms, ed. by D. Whitley, vol. 2 (Morgan Kaufmann, Burlington, 1993), pp. 5–17

    Google Scholar 

  11. T. El-Mihoub, A. Hopgood, L. Nolle, A. Battersby, Hybrid genetic algorithms: a review. Eng. Lett. 13(2), 124–137 (2006)

    Google Scholar 

  12. I.P. Gent, T. Walsh, Towards an understanding of hill-climbing procedures for SAT, in The National Conference on Artificial Intelligence (AAAI) (MIT Press, Cambridge, 1993), pp. 28–33

    Google Scholar 

  13. C. Glass, A. Prugel-Bennett, Genetic algorithm for graph coloring: exploration of Galinier and Hao’s algorithm. J. Comb. Optim. 7(3), 229–236 (2003)

    Google Scholar 

  14. D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, Reading, 1989)

    Google Scholar 

  15. D. Goldberg, Genetic algorithms and Walsh functions: part I, a gentle introduction. Complex Syst. 3, 129–152 (1989)

    Google Scholar 

  16. D. Goldberg, A note on Boltzmann tournament selection for genetic algorithms and population-oriented simulated annealing. Complex Syst. 4(4), 445–460 (1990)

    Google Scholar 

  17. D. Goldberg, K. Deb, A comparative analysis of selection schemes used in genetic algorithms, in Foundations of Genetic Algorithms, ed. by G. Rawlins, vol. 1 (Morgan Kaufmann, Burlington, 1991), pp. 69–93

    Google Scholar 

  18. D. Hains, D. Whitley, A. Howe, Improving Lin-Kernighan-Helsgaun with crossover on clustered instances of the TSP, in Parallel Problem Solving from Nature (PPSN) (Springer, Berlin, 2012), pp. 388–397

    Google Scholar 

  19. N. Hansen, The CMA evolution strategy: a comparing review, in Toward a New Evolutionary Computation (Springer, Berlin, 2006), pp. 75–102

    Google Scholar 

  20. N. Hansen, S. Kern, Evaluating the CMA evolution strategy on multimodal test functions, in Parallel Problem Solving from Nature (PPSN) (Springer, Berlin, 2004), pp. 282–291

    Google Scholar 

  21. R.B. Heckendorn, Embedded landscapes. Evol. Comput. 10(4), 345–369 (2002)

    Google Scholar 

  22. K. Helsgaun, An effective implementation of the Lin-Kernighan traveling salesman heuristic. Eur. J. Oper. Res. 126(1), 106–130 (2000)

    Google Scholar 

  23. K. Helsgaun, General k-opt submoves for the Lin-Kernighan TSP heuristic. Math. Program. Comput. 1(2–3), 119–163 (2009)

    Google Scholar 

  24. K. Helsgaun, DIMACS TSP challenge results: current best tours found by LKH (2013). http://www.akira.ruc.dk/keld/research/LKH/DIMACSresults.html. November 24, 2013

  25. G. Ho, P. Ji, H. Lau, A hybrid genetic algorithm for multi-depot vehicle routing problem. Eng. Appl. Artif. Intell. 21(4), 548–557 (2008)

    Google Scholar 

  26. J. Holland, Adaptation in Natural and Artificial Systems (University of Michigan Press, Ann Arbor, 1975)

    Google Scholar 

  27. J. Holland, Adaptation in Natural and Artificial Systems, 2nd edn. (MIT Press, Cambridge, 1992)

    Google Scholar 

  28. H.H. Hoos, Th. Stützle, Stochastic Local Search: Foundations and Applications (Morgan Kaufman, Burlington, 2004)

    Google Scholar 

  29. S.A. Kauffman, Adaptation on rugged fitness landscapes, in Lectures in the Science of Complexity, ed. by D.L. Stein (Addison-Wesley, Boston, 1989), pp. 527–618

    Google Scholar 

  30. S.A. Kauffman, The Origins of Order (Oxford Press, Oxford, 1993)

    Google Scholar 

  31. L. Kotthoff, P. Kerschke, H. Hoos, H. Trautmann, Improving the state of the art in inexact TSP solving using per-instance algorithm selection, in International Conference on Learning and Intelligent Optimization (Springer, Berlin, 2015), pp. 202–217

    Google Scholar 

  32. S. Lin, B. Kernighan, An effective heuristic algorithm for the traveling salesman problem. Oper. Res. 21, 498–516 (1973)

    Google Scholar 

  33. Z. Lü, J.K. Hao, A memetic algorithm for graph coloring. Eur. J. Oper. Res. 203(1), 241–250 (2010)

    Google Scholar 

  34. M. Mitchell, S. Forrest, Fitness landscapes: royal road functions, in Handbook of Evolutionary Computation, ed. by T. Bäck, D. Fogel, Z. Michalewicx, vol. B2.7 (Institute of Physics Publishing, Bristol, 1997), pp. 1–25

    Google Scholar 

  35. A. Möbius, B. Freisleben, P. Merz, M. Schreiber, Combinatorial optimization by iterative partial transcription. Phys. Rev. E 59(4), 4667–4674 (1999)

    Google Scholar 

  36. P. Moscato, C. Cotta, A gentle introduction to memetic algorithms, in Handbook of Metaheuristics, ed. by F. Glover, G. Kochenberger (Springer, Boston, 2003), pp. 105–144

    Google Scholar 

  37. Y. Nagata, S. Kobayashi, Edge assembly crossover: a high-power genetic algorithm for the traveling salesman problem, in International Conference on Genetic Algorithms (ICGA), ed. by T. Bäck (Morgan Kaufmann, Burlington, 1997), pp. 450–457

    Google Scholar 

  38. Y. Nagata, S. Kobayashi, A powerful genetic algorithms using edge assemble crossover the traveling salesman problem. INFORMS J. Comput. 25(2), 346–363 (2013)

    Google Scholar 

  39. Y. Nagata, D. Soler, A new genetic algorithm for the asymmetric TSP. Expert Syst. Appl. 39(10), 8947–8953 (2012)

    Google Scholar 

  40. A. Nix, M. Vose, Modelling genetic algorithms with Markov chains. Ann. Math. Artif. Intell. 5, 79–88 (1992)

    Google Scholar 

  41. S. Rana, R. Heckendorn, D. Whitley, A tractable Walsh analysis of SAT and its implications for genetic algorithms, in The National Conference on Artificial Intelligence (AAAI) (MIT Press, Cambridge, 1998), pp. 392–397

    Google Scholar 

  42. C. Rego, D. Gamboa, F. Glover, C. Osterman, Traveling salesman problem heuristics: leading methods, implementations and latest advances. Eur. J. Oper. Res. 211(3), 427–441 (2011)

    Google Scholar 

  43. C.M. Reidys, P.F. Stadler, Combinatorial landscapes. SIAM Rev. 44, 3–54 (2002)

    Google Scholar 

  44. B. Selman, H. Levesque, D. Mitchell, A new method for solving hard satisfiability problems, in The National Conference on Artificial Intelligence (AAAI), San Jose (1992), pp. 44–446

    Google Scholar 

  45. A. Sokolov, D. Whitley, Unbiased tournament selection, in Genetic and Evolutionary Computation Conference (GECCO) (ACM, New York, 2005), pp. 1131–1138

    Google Scholar 

  46. A. Sokolov, D. Whitley, A note on the variance of rank-based selection strategies for genetic algorithms and genetic programming. Genet. Program Evolvable Mach. 8(3), 221–237 (2007)

    Google Scholar 

  47. K. Sorensen, Metaheuristics: the metaphor exposed. Int. Trans. Oper. Res. 22(1), 3–18 (2015)

    Google Scholar 

  48. A.M. Sutton, A. Howe, D. Whitley, A theoretical analysis of the k-satisfiability search space, in Workshop on Engineering Stochastic Local Search Algorithms (SLS) (2009), pp. 46–60

    Google Scholar 

  49. G. Syswerda, Reproduction in generational and steady state genetic algorithms, in Foundations of Genetic Algorithms, ed. by G. Rawlins, vol. 1 (Morgan Kaufmann, Burlington, 1991), pp. 94–101

    Google Scholar 

  50. R. Tinós, D. Whitley, G. Ochoa, Generalized asymmetric partition crossover (GAPX) for the asymmetric TSP, in Genetic and Evolutionary Computation Conference (GECCO) (ACM, New York, 2014), pp. 501–508

    Google Scholar 

  51. R. Tinós, D. Whitley, F. Chicano, Partition crossover for pseudo-Boolean optimization, in Foundations of Genetic Algorithms (FOGA-15) (2015), pp. 137–149

    Google Scholar 

  52. N. Veerapen, G. Ochoa, D. Whitley, Tunneling crossover for the asymmetric TSP, in Parallel Problem Solving from Nature (PPSN). Lecture Notes in Computer Science (Springer, Cham, 2016), pp. 994–1004

    Google Scholar 

  53. T. Vidal, T. Crainic, M. Gendreau, N. Lahrichi, W. Rei, A hybrid genetic algorithm for multi-depot and periodic vehicle routing problems. Oper. Res. 60(3), 611–624 (2012)

    Google Scholar 

  54. M. Vose, Modeling simple genetic algorithms, in Foundations of Genetic Algorithms (FOGA 2), ed. by D. Whitley (Morgan Kaufmann, Burlington, 1993), pp. 63–73

    Google Scholar 

  55. M. Vose, The Simple Genetic Algorithm (MIT Press, Cambridge, 1999)

    Google Scholar 

  56. J. Watson, C. Ross, V. Eisele, J. Denton, J. Bins, C. Guerra, D. Whitley, The traveling Salesrep problem, edge assembly crossover, and 2-opt, in Parallel Problem Solving from Nature (PPSN) (Springer, Berlin, 1998), pp. 823–832

    Google Scholar 

  57. D. Whitley, An executable model of the simple genetic algorithm, in Foundations of Genetic Algorithms (FOGA 2) ed. by D. Whitley (Morgan Kaufmann, Burlington, 1993), pp. 45–62

    Google Scholar 

  58. D. Whitley, A genetic algorithm tutorial. Stat. Comput. 4, 65–85 (1994)

    Google Scholar 

  59. D. Whitley, A review of models for simple and cellular genetic algorithms, in Applications of Modern Heuristic Search, ed. by V.J. Rayward-Smith, Chap. 4 (Alfred Waller Limited, Oxon, 1995), pp. 55–67

    Google Scholar 

  60. D. Whitley, W. Chen, Constant time steepest descent local search with lookahead for NK-landscapes and MAX-kSAT, in Genetic and Evolutionary Computation Conference (GECCO) (ACM, New York, 2012), pp. 1357–1364

    Google Scholar 

  61. D. Whitley, J. Kauth, GENITOR: a different genetic algorithm, in Proceedings of the Rocky Mountain Conference on Artificial Intelligence (1988), pp. 118–130

    Google Scholar 

  62. D. Whitley, A. Sutton, Genetic algorithms: a survey of models and methods, in Handbook of Natural Computation (Springer, Berlin, 2013), pp. 637–671

    Google Scholar 

  63. D. Whitley, S. Rana, R. Heckendorn, The island model genetic algorithm: on separability, population size and convergence. J. Comput. Inf. Technol. 7(1), 33–47 (1999)

    Google Scholar 

  64. D. Whitley, A. Sutton, A.E. Howe, L. Barbulescu, Resource scheduling with permutation based representations: three applications, in Evolutionary Computation in Practice, ed. by T. Yu, L. Davis, C. Baydar, R. Roy. Studies in Computational Intelligence, vol. 88 (Springer, Berlin, 2008), pp. 219–243

    Google Scholar 

  65. D. Whitley, D. Hains, A. Howe, A hybrid genetic algorithm for the traveling salesman problem using generalized partition crossover, in Parallel Problem Solving from Nature (PPSN) (Springer, Berlin, 2010), pp. 566–575

    Google Scholar 

  66. D. Whitley, A. Howe, D. Hains, Greedy or not? Best improving versus first improving stochastic local search for MAXSAT, in The National Conference on Artificial Intelligence (AAAI) (2013), pp. 940–946

    Google Scholar 

  67. D. Whitley, F. Chicano, B. Goldman, Gray box optimization for Mk landscapes (NK landscapes and MAX-kSAT). Evol. Comput. 24, 491–519 (2016)

    Google Scholar 

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Authors and Affiliations

  1. Colorado State University, Fort Collins, CO, USA

    Darrell Whitley

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  1. Darrell Whitley
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  1. Department of Mathematics and Industrial Engineering, Polytechnique Montréal, Montreal, QC, Canada

    Michel Gendreau

  2. Département d’informatique et de recherche opérationnelle, Université de Montréal, Montreal, QC, Canada

    Jean-Yves Potvin

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Whitley, D. (2019). Next Generation Genetic Algorithms: A User’s Guide and Tutorial. In: Gendreau, M., Potvin, JY. (eds) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol 272. Springer, Cham. https://doi.org/10.1007/978-3-319-91086-4_8

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