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An introduction to genetic algorithms

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Abstract

Genetic algorithms (GAs) are search and optimization tools, which work differently compared to classical search and optimization methods. Because of their broad applicability, ease of use, and global perspective, GAs have been increasingly applied to various search and optimization problems in the recent past. In this paper, a brief description of a simple GA is presented. Thereafter, GAs to handle constrained optimization problems are described. Because of their population approach, they have also been extended to solve other search and optimization problems efficiently, including multimodal, multiobjective and scheduling problems, as well as fuzzy-GA and neuro-GA implementations. The purpose of this paper is to familiarize readers to the concept of GAs and their scope of application.

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References

  • Bäck T, Fogel D, Michalewicz Z (eds) 1997Handbook of evolutionary computation. (New York: Inst. Phys. Publ. and Oxford Univ. Press)

    MATH  Google Scholar 

  • Box M J 1965 A new method of constrained optimization and a comparison with other methods.Comput. J. 8: 42–52

    MATH  MathSciNet  Google Scholar 

  • Chaturvedi D, Deb K, Chakrabarty S K 1995 Structural optimization using real-coded genetic algorithms. In:Proceedings of the Symposium on Genetic Algorithms (eds) P K Roy, S D Mehta (Dehra Dun: Siva Offset Press) pp 73–82

    Google Scholar 

  • Davis L 1991Handbook of genetic algorithms (New York: Van Nostrand Reinhold)

    Google Scholar 

  • Dawkins R 1976The selfish gene (New York: Oxford University Press)

    Google Scholar 

  • Dawkins R 1986The blind watchmaker (New York: Penguin).

    Google Scholar 

  • Deb K 1989Genetic algorithms in multimodal function optimization. Master’s thesis (TCGA Report No. 89002), Tuscaloosa, University of Alabama

  • Deb K 1995Optimization for engineering design: Algorithms and examples (Delhi: Prentice-Hall)

    Google Scholar 

  • Deb K, Agrawal R B 1995 Simulated binary crossover for continuous search space.Complex Syst. 9: 115–148

    MATH  MathSciNet  Google Scholar 

  • Deb K, Goldberg D E 1989 An investigation of niche and species formation in genetic function optimizationProceedings of the Third International Conference on Genetic Algorithms, pp. 42–50

  • Deb K, Goyal M 1999 A robust optimization procedure for mechanical component design based on genetic adaptive search.ASME J. Mech. Design

  • Deb K, Kumar A 1995 Real-coded genetic algorithms with simulated binary crossover: Studies on multimodal and multiobjective problems.Complex Syst. 9: 431–454

    Google Scholar 

  • Deb K, Pratihar D K, Ghosh A 1998 Learning to avoid moving obstracles optimally for mobile robots using a genetic-fuzzy approach.Parallel Problem Solving from Nature, (eds) A E Eiben, T Bäck, M Schoenauer, H-P Schweftel, 5: 583–592

    Google Scholar 

  • Eldredge N 1989Macro-evolutionary dynamics: Species, niches, and adaptive peaks (New York: McGraw-Hill)

    Google Scholar 

  • Eshelman L, Schaffer J D 1993 Real-coded genetic algorithms and interval-schemata.Foundations of Genetic Algorithms (ed.) L D Whitley, 2: 187–202

  • Fonseca C M, Fleming P J 1993 Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. InProceedings of the Fifth International Conference on Genetic Algorithms (ed.) S Forrest, pp 416–423

  • Gen M, Cheng R 1997Genetic algorithms and engineering design (New York: Wiley)

    Google Scholar 

  • Goldberg D E 1989Genetic algorithms in search, optimization, and machine learning (New York: Addison-Wesley)

    MATH  Google Scholar 

  • Goldberg D E, Deb K 1991 A comparison of selection schemes used in genetic algorithms. InFoundations of genetic algorithms (ed.) G J E Rawlins (San Mateo, CA: Morgan Kaufmann) pp 69–93

    Google Scholar 

  • Goldberg D E, Deb K, Horn J 1992 Massive multimodality, deception, and genetic algorithms. InParallel Problem Solving from Nature (eds) R Manner, B Manderick (Berlin: Springer-Verlag) 2: 37–46

    Google Scholar 

  • Goldberg D E, Richardson J 1987 Genetic algorithms with sharing for multimodal function optimization.Proceedings of the Second International Conference on Genetic Algorithms (ed.) J J Grefenstette, pp 41–49

  • Goldberg D E, Smith R 1987 Nonstationary function optimization using genetic algorithms with dominance and diploidy. InProceedings of the Second International Conference on Genetic Algorithms (ed.) J J Grefenstette (Mahwah, NJ: Lawrence Erlbaum) pp 59–68

    Google Scholar 

  • Herrera F, Verdegay J L (eds) 1996Genetic algorithms and soft computing (Heidelberg: Physica-Verlag)

    Google Scholar 

  • Holland J H 1975Adaptation in natural and artificial systems (Ann Arbor: Univ. of Michigan Press)

    Google Scholar 

  • Horn J, Nafpliotis N 1993 Multiobjective optimization using niched Pareto genetic algorithms. IlliGAL Report No 93005, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL

  • Karr C 1991Design of an adaptive fuzzy logic controller using a genetic algorithm. InProceedings of the Fourth International Conference on Genetic Algorithms (eds) R K Belew, L B Booker (San Mateo, CA: Morgan Kaufmann) pp 450–457

    Google Scholar 

  • McClelland J L, Rumelhart D E 1988Parallel distributed processing (Cambridge: MIT Press) vol. 1 and 2

    Google Scholar 

  • Michalewicz Z 1992Genetic algorithms + data structures = evolution programs (Berlin: Springer-Verlag)

    MATH  Google Scholar 

  • Miller G F, Todd P M, Hedge S U 1989 Designing neural networks using genetic algorithms.Proceedings of the Third International Conference on Genetic Algorithms (ed.) J D Schaffer (San Mateo, CA: Morgan Kaufmann) pp 379–384

    Google Scholar 

  • Mitchell M 1996Introduction to genetic algorithms (Ann Arbor, MI: Univ. of Michigan Press) (also New Delhi: Prentice-Hall)

    Google Scholar 

  • Reklaitis G V, Ravindran A, Ragsdell K M 1983Engineering optimization methods and applications (New York: John Wiley and Sons)

    Google Scholar 

  • Rudolph G 1994 Convergence analysis of canonical genetic algorithms.IEEE Trans. Neural Network NN-5: 96–101

    Article  Google Scholar 

  • Sandgren E, Jensen E 1990 Topological design of structural components using genetic optimization methods.Proceedings of the 1990 Winter Annual Meeting of the ASME, AMD-115: 31–43

    Google Scholar 

  • Spears W M, De Jong K A 1991 An analysis of multi-point crossover. InFoundations of genetic algorithms (ed.) C J E Rawlins pp 310–315 (San Mateo, CA: Morgan Kaufmann)

    Google Scholar 

  • Srinivas N 1994Multiobjective optimization using nondominated sorting in genetic algorithms. Masters thesis, Indian Institute of Technology, Kanpur

    Google Scholar 

  • Srinivas N, Deb K 1995 Multiobjective function optimization using nondominated sorting genetic algorithms.Evolutionary Comput. 2: pp 221–248

    Google Scholar 

  • Starkweather T, McDaniel S, Mathias K, Whitley D, Whitley C 1991 A comparison of genetic scheduling operators. InProceedings of the Fourth International Conference on Genetic Algorithms (eds) R Belew, L B Booker (San Mateo, CA: Morgan Kaufmann) pp 69–76

    Google Scholar 

  • Vose M D 1990 Generalizing the notion of schema in genetic algorithms.Artif. Intell. 50: 385–396

    Article  MathSciNet  Google Scholar 

  • Winter G, Périaux J, Galan M, Cuesta P (eds) 1996Genetic algorithms in engineering and computer science (Chichester: Wiley)

    Google Scholar 

  • Whitley D 1992 An executable model of a simple genetic algorithm. InFoundations of genetic algorithms, vol. 2 pp 45–62

    Google Scholar 

Download references

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Authors and Affiliations

  1. Kanpur Genetic Algorithms Laboratory (KanGAL), Department of Mechanical Engineering, Indian Institute of Technology Kanpur, 208 016, Kanpur, India

    Kalyanmoy Deb

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  1. Kalyanmoy Deb
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Correspondence to Kalyanmoy Deb.

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This paper was written while the author was visiting the University of Dortmund, Germany on an Alexander von Humboldt fellowship.

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Deb, K. An introduction to genetic algorithms. Sadhana 24, 293–315 (1999). https://doi.org/10.1007/BF02823145

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