Abstract
This chapter first reviews the simple genetic algorithm. Mathematical models of the genetic algorithm are also reviewed, including the schema theorem, exact infinite population models, and exact Markov models for finite populations. The use of bit representations, including Gray encodings and binary encodings, is discussed. Selection, including roulette wheel selection, rank-based selection, and tournament selection, is also described. This chapter then reviews other forms of genetic algorithms, including the steady-state Genitor algorithm and the CHC (cross-generational elitist selection, heterogenous recombination, and cataclysmic mutation) algorithm. Finally, landscape structures that can cause genetic algorithms to fail are looked at, and an application of genetic algorithms in the domain of resource scheduling, where genetic algorithms have been highly successful, is also presented.
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References
Bäck T (1996) Evolutionary algorithms in theory and practice. Oxford University Press, Oxford
Baker J (1987) Reducing bias and inefficiency in the selection algorithm. In: Grefenstette J (ed) GAs and their applications: 2nd international conference, Erlbaum, Hilsdale, NJ, pp 14–21
Bitner JR, Ehrlich G, Reingold EM (1976) Efficient generation of the binary reflected gray code and its applications. Commun ACM 19(9):517–521
Brent R (1973) Algorithms for minization with derivatives. Dover, Mineola, NY
Bridges C, Goldberg D (1987) An analysis of reproduction and crossover in a binary coded genetic algorithm. In: Grefenstette J (ed) GAs and their applications: 2nd international conference, Erlbaum, Cambridge, MA
Davis L (1985a) Applying adaptive algorithms to epistatic domains. In: Proceedings of the IJCAI-85, Los Angeles, CA
Davis L (1985b) Job shop scheduling with genetic algorithms. In: Grefenstette J (ed) International conference on GAs and their applications. Pittsburgh, PA, pp 136–140
Davis L (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, New York
DeJong K (1993) Genetic algorithms are NOT function optimizers. In: Whitley LD (ed) FOGA – 2, Morgan Kaufmann, Los Altos, CA, pp 5–17
Eshelman L (1991) The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination. In: Rawlins G (ed) FOGA – 1, Morgan Kaufmann, Los Altos, CA, pp 265–283
Eshelman L, Schaffer D (1991) Preventing premature convergence in genetic algorithms by preventing incest. In: Booker L, Belew R (eds) Proceedings of the 4th international conference on GAs. Morgan Kaufmann, San Diego, CA
Goldberg D (1987) Simple genetic algorithms and the minimal, deceptive problem. In: Davis L (ed) Genetic algorithms and simulated annealing. Pitman/Morgan Kaufmann, London, UK, chap 6
Goldberg D (1989a) Genetic algorithms and Walsh functions: Part II, deception and its analysis. Complex Syst 3:153–171
Goldberg D (1989b) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading, MA
Goldberg D (1990) A note on Boltzmann tournament selection for genetic algorithms and population-oriented simulated annealing. Tech. Rep. Nb. 90003. Department of Engineering Mechanics, University of Alabama, Tuscaloosa, AL
Goldberg D, Deb K (1991) A comparative analysis of selection schemes used in genetic algorithms. In: Rawlins G (ed) FOGA – 1, Morgan Kaufmann, San Mateo, CA, pp 69–93
Goldberg D, Lingle R (1985) Alleles, loci, and the traveling salesman problem. In: Grefenstette J (ed) International conference on GAs and their applications. London, UK, pp 154–159
Grefenstette J (1993) Deception considered harmful. In: Whitley LD (ed) FOGA – 2, Morgan Kaufmann, Vail, CO, pp 75–91
Hansen N (2006) The CMA evolution strategy: a comparing review. In: Toward a new evolutionary computation: advances on estimation of distribution algorithms. Springer, Heidelberg, Germany, pp 75–102
Hansen N (2008) Adaptive encoding: how to render search coordinate system invariant. In: Proceedings of 10th international conference on parallel problem solving from nature. Springer, Dortmund, Germany, pp 205–214
Heckendorn R, Rana S, Whitley D (1999a) Polynomial time summary statistics for a generalization of MAXSAT. In: GECCO-99, Morgan Kaufmann, San Francisco, CA, pp 281–288
Heckendorn R, Rana S, Whitley D (1999b) Test function generators as embedded landscapes. In: Foundations of genetic algorithms FOGA – 5, Morgan Kaufmann, Los Atlos, CA
Heckendorn RB, Whitley LD, Rana S (1996) Nonlinearity, Walsh coefficients, hyperplane ranking and the simple genetic algorithm. In: FOGA – 4, San Diego, CA
Ho Y (1994) Heuristics, rules of thumb, and the 80/20 proposition. IEEE Trans Automat Cont 39(5):1025–1027
Ho Y, Sreenivas RS, Vakili P (1992) Ordinal optimization of discrete event dynamic systems. Discrete Event Dyn Syst 2(1):1573–7594
Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, MI
Holland JH (1992) Adaptation in natural and artificial systems, 2nd edn. MIT Press, Cambridge, MA
Schaffer JD, Eshelman L (1993) Real-coded genetic algorithms and interval schemata. In: Whitley LD (ed) FOGA – 2, Morgan Kaufmann, Los Atlos, CA
Mathias KE, Whitley LD (1994) Changing representations during search: a comparative study of delta coding. J Evolut Comput 2(3):249–278
Nagata Y, Kobayashi S (1997) Edge assembly crossover: a high-power genetic algorithm for the traveling salesman problem. In: Bäck T (ed) Proceedings of the 7th international conference on GAs, Morgan Kaufmann, California, pp 450–457
Nix A, Vose M (1992) Modelling genetic algorithms with Markov chains. Ann Math Artif Intell 5:79–88
Poli R (2005) Tournament selection, iterated coupon-collection problem, and backward-chaining evolutionary algorithms. In: Foundations of genetic algorithms, Springer, Berlin, Germany, pp 132–155
Radcliffe N, Surry P (1995) Fundamental limitations on search algorithms: evolutionary computing in perspective. In: van Leeuwen J (ed) Lecture notes in computer science, vol 1000, Springer, Berlin, Germany
Rana S, Whitley D (1997) Representations, search and local optima. In: Proceedings of the 14th national conference on artificial intelligence AAAI-97. MIT Press, Cambridge, MA, pp 497–502
Rana S, Heckendorn R, Whitley D (1998) A tractable Walsh analysis of SAT and its implications for genetic algorithms. In: AAAI98, MIT Press, Cambridge, MA, pp 392–397
Rosenbrock H (1960) An automatic method for finding the greatest or least value of a function. Comput J 3:175–184
Salomon R (1960) Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions. Biosystems 39(3):263–278
Schaffer JD (1987) Some effects of selection procedures on hyperplane sampling by genetic algorithms. In: Davis L (ed) Genetic algorithms and simulated annealing. Morgan Kaufmann, San Francisco, CA, pp 89–130
Schwefel HP (1981) Numerical optimization of computer models. Wiley, New York
Schwefel HP (1995) Evolution and optimum seeking. Wiley, New York
Sokolov A, Whitley D (2005) Unbiased tournament selection. In: Proceedings of the 7th genetic and evolutionary computation conference. The Netherlands, pp 1131–1138
Spears W, Jong KD (1991) An analysis of multi-point crossover. In: Rawlins G (ed) FOGA – 1, Morgan Kaufmann, Los Altos, CA, pp 301–315
Starkweather T, Whitley LD, Mathias KE (1990) Optimization using distributed genetic algorithms. In: Schwefel H, Männer R (eds) Parallel problem solving from nature. Springer, London, UK, pp 176–185
Starkweather T, McDaniel S, Mathias K, Whitley D, Whitley C (1991) A comparison of genetic sequencing operators. In: Booker L, Belew R (eds) Proceedings of the 4th international conference on GAs. Morgan Kaufmann, San Mateo, CA, pp 69–76
Suh J, Gucht DV (1987) Distributed genetic algorithms. Tech. rep., Indiana University, Bloomington, IN
Syswerda G (1989) Uniform crossover in genetic algorithms. In: Schaffer JD (ed) Proceedings of the 3rd international conference on GAs, Morgan Kaufmann, San Mateo, CA
Syswerda G (1991) Schedule optimization using genetic algorithms. In: Davis L (ed) Handbook of genetic algorithms, Van Nostrand Reinhold, New York, chap 21
Syswerda G, Palmucci J (1991) The application of genetic algorithms to resource scheduling. In: Booker L, Belew R (eds) Proceedings of the 4th international conference on GAs, Morgan Kaufmann, San Mateo, CA
Vose M (1993) Modeling simple genetic algorithms. In: Whitley LD (ed) FOGA – 2, Morgan Kaufmann, San Mateo, CA, pp 63–73
Vose M (1999) The simple genetic algorithm. MIT Press, Cambridge, MA
Vose M, Liepins G (1991) Punctuated equilibria in genetic search. Complex Syst 5:31–44
Vose M, Wright A (1997) Simple genetic algorithms with linear fitness. Evolut Comput 2(4):347–368
Watson JP, Rana S, Whitley D, Howe A (1999) The impact of approximate evaluation on the performance of search algorithms for warehouse scheduling. J Scheduling 2(2):79–98
Whitley D (1999) A free lunch proof for gray versus binary encodings. In: GECCO-99, Morgan Kaufmann, Orlando, FL, pp 726–733
Whitley D, Kauth J (1988) GENITOR: A different genetic algorithm. In: Proceedings of the 1988 Rocky Mountain conference on artificial intelligence, Denver, CO
Whitley D, Rowe J (2008) Focused no free lunch theorems. In: GECCO-08, ACM Press, New York
Whitley D, Yoo NW (1995) Modeling permutation encodings in simple genetic algorithm. In: Whitley D, Vose M (eds) FOGA – 3, Morgan Kaufmann, San Mateo, CA
Whitley D, Starkweather T, Fuquay D (1989) Scheduling problems and traveling salesmen: the genetic edge recombination operator. In: Schaffer JD (ed) Proceedings of the 3rd international conference on GAs. Morgan Kaufmann, San Francisco, CA
Whitley D, Das R, Crabb C (1992) Tracking primary hyperplane competitors during genetic search. Ann Math Artif Intell 6:367–388
Whitley D, Beveridge R, Mathias K, Graves C (1995a) Test driving three 1995 genetic algorithms. J Heuristics 1:77–104
Whitley D, Mathias K, Pyeatt L (1995b) Hyperplane ranking in simple genetic algorithms. In: Eshelman L (ed) Proceedings of the 6th international conference on GAs. Morgan Kaufmann, San Francisco, CA
Whitley D, Mathias K, Rana S, Dzubera J (1996) Evaluating evolutionary algorithms. Artif Intell J 85:1–32
Whitley LD (1989) The GENITOR algorithm and selective pressure: why rank based allocation of reproductive trials is best. In: Schaffer JD (ed) Proceedings of the 3rd international conference on GAs. Morgan Kaufmann, San Francisco, CA, pp 116–121
Whitley LD (1991) Fundamental principles of deception in genetic search. In: Rawlins G (ed) FOGA – 1, Morgan Kaufmann, San Francisco, CA, pp 221–241
Whitley LD (1993) An executable model of the simple genetic algorithm. In: Whitley LD (ed) FOGA – 2, Morgan Kaufmann, Vail, CO, pp 45–62
Wolpert DH, Macready WG (1995) No free lunch theorems for search. Tech. Rep. SFI-TR-95-02-010, Santa Fe Institute, Santa Fe, NM
Acknowledgments
This research was partially supported by a grant from the Air Force Office of Scientific Research, Air Force Materiel Command, USAF, under grant number FA9550-08-1-0422. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes, notwithstanding any copyright notation thereon. Funding was also provided by the Coors Brewing Company, Golden, Colorado.
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Whitley, D., Sutton, A.M. (2012). Genetic Algorithms — A Survey of Models and Methods. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds) Handbook of Natural Computing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92910-9_21
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