Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Genetic algorithms play a significant role, as search techniques forhandling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the naturalevolution principles of populations. These algorithms process apopulation of chromosomes, which represent search space solutions,with three operations: selection, crossover and mutation.

Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded genetic algorithms. Different models of genetic operators and some mechanisms available for studying the behaviour of this type of genetic algorithms are revised and compared.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Antonisse, J. (1989). A new interpretation of schema notation that overturns the binary encoding constraint. Proc. of the Third Int. Conf. on Genetic Algorithms, J. David Schaffer (Ed.) (Morgan Kaufmann Publishers, San Mateo), 86–91.

    Google Scholar 

  • Arabas, J., Mulawka, J.J. & Pokraśniewicz, J. (1995). A New Class of the Crossover Operators for the Numerical Optimization. Proc. of the Sixth Int. Conf. on Genetic Algorithms, L. Eshelman (Ed.) (Morgan Kaufmann Publishers, San Francisco), 42–47.

    Google Scholar 

  • Bäck, T., Hoffmeister, F. & Schwefel, HP. (1991a). A Survey of Evolution Strategies. Proc. of the Fourth Int. Conf. on Genetic Algorithms, R. Belew and L.B. Booker (Eds.) (Morgan Kaufmmann, San Mateo), 2–9.

    Google Scholar 

  • Bäck, T., Hoffmeister, F. & Schwefel, HP. (1991b). Extended Selection Mechanisms in Genetic Algorithms. Proc. of the Fourth Int. Conf. on Genetic Algorithms, R. Belew and L.B. Booker (Eds.) (Morgan Kaufmmann, San Mateo), 92–99.

    Google Scholar 

  • Bäck, T., Schwefel, H-P. (1993). An Overview of Evolutionary Algorithms for Parameter Optimization. Evolutionary Computation, 1(1), 1–23.

    Google Scholar 

  • Baker, J.E. (1985). Adaptive Selection Methods for Genetic Algorithms. Proc. of an Int. Conf. on Genetic Algorithms(L. Erlbaum Associates, Hillsdale, MA), 101–111.

    Google Scholar 

  • Baker. J. E. (1987). Reducing bias and inefficiency in the selection algorithm. Proc. Second Int. Conf. on Genetic Algorithms(L. Erlbaum Associates, Hillsdale, MA), 14–21.

    Google Scholar 

  • Beasley, D., Bull, D.R. & Martin, R. R. (1993). An Overview of Genetic Algorithms: Part 2, Research Topics. University Computing, 15(4), 170–181.

    Google Scholar 

  • Belew, R.K. and Booker, L.B., (Eds.) (1991). Proceeding of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann Publishers.

  • Booker, L.B., Goldberg, D.E. & Holland, J.H. (1989). Classifier Systems and Genetic Algorithms. Artificial Intelligence 40(1/3), 235–282.

    Google Scholar 

  • Bramlette, M.F. (1991). Initialization, Mutation and Selection Methods in Genetic Algorithms for Function Optimization. Proc. of the Fourth Int. Conf. on Genetic Algorithms, R. Belew and L.B. Booker (Eds.) (Morgan Kaufmmann, San Mateo), 100–107.

    Google Scholar 

  • Bramlette, M.F. & Bouchard, E.E. (1991). Genetic Algorithms in Parametric Design of Aircraft. Handbook of Genetic Algorithms, L. Davis (Ed.) (Van Nostrand Reinhold, New York), 109–123.

    Google Scholar 

  • Caruana, R.A. & Schaffer, J.D. (1988). Representation and Hidden Bias: Gray versus Binary Coding for Genetic Algorithms. Proc. of the Fifth International Conference on Machine Learning, 153–162.

  • Corcoran, A.L. & Sen S. (1994). Using Real-Valued Genetic Algorithms to Evolve Sets for Classification. IEEE Conference on Evolutionary Computation, 120–124.

  • Cordon, O. & Herrera F. (1995). A General Study of Genetic Fuzzy Systems. Genetic Algorithms in Engineering and Computer Science, (Periaux, J., Winte, G., Eds.) John Wiley and Sons, 33–57.

  • Davidor, Y. (1991). Genetic Algorithms and Robotics: A Heuristic Strategy for Optimization. World Scientific, London.

    Google Scholar 

  • Davis, L. (1989). Adapting Operator Probabilities in Genetic Algorithms. Proc. of the Third Int. Conf. on Genetic Algorithms, J. David Schaffer (Ed.) (Morgan Kaufmann Publishers, San Mateo), 61–69.

    Google Scholar 

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

    Google Scholar 

  • Deb, K. & Kumar, A. (1995a). RealCoded Genetic Algorithms with Simulated Binary Crossover: Studies on Multimodal and Multiobjective Problems. Complex Systems 9, 431–454.

    Google Scholar 

  • Deb, K. & Agrawal, R.B. (1995b). Simulated Binary Crossover for Continuous Search Space. Complex Systems 9, 115–148.

    Google Scholar 

  • Deb, K. & Goyal, M. (1997). Optimization Engineering Designs Using a Combined Genetic Search. Proc. of the Seventh Int. Conf. on Genetic Algorithms, T. Bäck (Ed.) (Morgan Kaufmann Publishers, San Francisco). 521–528.

    Google Scholar 

  • De Jong, K.A. (1975). An analysis of the behavior of a class of genetic adaptive systems. Doctoral dissertation, University of Michigan.

  • Eshelman, L.J., Caruana, A. & Schaffer, J.D. (1989). Biases in the Crossover Landscape. Proc. of the Third Int. Conf. on Genetic Algorithms, J. David Schaffer (Ed.) (Morgan Kaufmann Publishers, San Mateo), 86–91.

    Google Scholar 

  • Eshelman L.J. & Schaffer J.D. (1993). Real-Coded Genetic Algorithms and Interval-Schemata. Foundation of Genetic Algorithms 2, L. Darrell Whitley (Ed.) (Morgan Kaufmann Publishers, San Mateo), 187–202.

    Google Scholar 

  • Eshelman, L.J., Mathias, K.E. & Schaffer, J.D. (1996). Convergence Controlled Variation. Foundations of Genetic Algorithms 4, R.K. Belew, M.D. Vose (Eds.) (Morgan Kaufmann Publishers, San Francisco), 203–224.

    Google Scholar 

  • Eshelman, L.J., Mathias, K.E. & Schaffer, J.D. (1997). Crossover Operator Biases: Exploiting the Population Distribution. Proc. of the Seventh Int. Conf. on Genetic Algorithms, T. Bäck (Ed.) (Morgan Kaufmann Publishers, San Francisco), 354–361.

    Google Scholar 

  • Fogel, D.B. (1994). An Introduction to Simulated Evolutionary Optimization. IEEE Trans. on Neural Networks 5(1), 3–14.

    Google Scholar 

  • Forrest, S. (Ed.) (1993). Proceeding of the Fifth International Conference on Genetic Algorithms. Morgan Kaufmann Publishers, San Mateo.

    Google Scholar 

  • Forrest, S., Javornik, B., Smith, R.E. & Perlson, A.S. (1993). Using Genetic Algorithms to Explore Pattern Recognition in the Immune System. Evolutionary Computation 1, 191–212.

    Google Scholar 

  • Fox, B.R. & McMahon, M.B. (1991). Genetic Operators for Sequencing Problems. Foundations of Genetic Algorithms 1, G.J.E. Rawlin (Ed.) (Morgan Kaufmann, San Mateo), 284–300.

    Google Scholar 

  • Goldberg, D.E. (1989a). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, New York.

    Google Scholar 

  • Goldberg D.E. (1989b). Genetic Algorithms and Walsh Functions: Part II, Deception and Its Analysis. Complex Systems 3, 153–171.

    Google Scholar 

  • Goldberg D.E., Korb, B. & Deb, K. (1989). Messy Genetic Algorithms: Motivation, Analysis, and First Results. Complex Systems 3, 493–530.

    Google Scholar 

  • Goldberg D.E. (1991a). Real-Coded Genetic Algorithms, Virtual Alphabets, and Blocking. Complex Systems 5, 139–167.

    Google Scholar 

  • Goldberg, D.E. & Deb, K. (1991b). A Comparative Analysis of Selection Schemes Used in Genetic Algorithms. Foundations of Genetic Algorithms 1, G.J.E. Rawlin (Ed.) (Morgan Kaufmann, San Mateo), 69–93.

    Google Scholar 

  • Goldberg, D.E. (1991c). Genetic and Evolutionary Algorithms Come of Age. Communication of the Association for Computing Machinery 37(3), 113–119.

    Google Scholar 

  • Grefenstette J.J. (1990). A User’s Guide to GENESIS Version 5.0.

  • Grefenstette J.J. (Ed.) (1995). Genetic Algorithms for Machine Learning. Kluwer Academic Publishers, Boston (Reprinted from Machine Learning 13(2/3), 1993).

    Google Scholar 

  • Hart, W.E. (1996). A Stationary Point Convergence Theory for Evolutionary Algorithms. Foundations of Genetic Algorithms 4, R.K. Belew, M.D. Vose (Eds.) (Morgan Kaufmann Publishers, San Francisco), 325–342.

    Google Scholar 

  • Hart, W.E. (1997). A Generalized Stationary Point Convergence Theory for Evolutionary Algorithms. Proc. of the Seventh Int. Conf. on Genetic Algorithms, T. Bäck (Ed.) (Morgan Kaufmann Publishers, San Francisco), 127–134.

    Google Scholar 

  • Herrera, F, Herrera-Viedma, E., Lozano, M. & Verdegay, J.L. (1994). Fuzzy Tools to Improve Genetic Algorithms. Proc. Second European Congress on Intelligent Techniques and Soft Computing, 1532–1539.

  • Herrera, F, Lozano, M. & Verdegay, J.L. (1995). Tuning Fuzzy Logic Controllers by Genetic Algorithms. International Journal of Approximate Reasoning 12 299–315.

    Google Scholar 

  • Herrera, F., Lozano, M. & Verdegay, J.L. (1996a). Dynamic and Heuristic Fuzzy Connectives Based Crossover Operators for Controlling the Diversity and Convergence of Real Coded Genetic Algorithms. Int. Journal of Intelligent Systems 11, 1013–1041.

    Google Scholar 

  • Herrera, F. & Lozano, M. (1996b). Adaptation of Genetic Algorithm Parameters Based on Fuzzy Logic Controllers. Genetic Algorithms and Soft Computing, F. Herrera, J.L. Verdegay (Eds.), Physica-Verlag, 95–125.

  • Herrera, F. & Lozano, M. (1996c). Heuristic Crossover for Real-Coded Genetic Algorithms Based on Fuzzy Connectives. 4th International Conference on Parallel Problem Solving from Nature, HM. Voigt, W. Ebeling, I. Rechenberg, HP. Schwefel (Eds.), LNCS 1141, Springer, 336–345.

  • Herrera, F., Lozano, M. & Verdegay, J.L. (1997a). Fuzzy Connectives Based Crossover Operators to Model Genetic Algorithms Population Diversity. Fuzzy Sets and Systems 92(1), 21–30.

    Google Scholar 

  • Herrera, F. & Lozano, M. (1997b). Heterogeneous Distributed Genetic Algorithms Based on the Crossover Operator. Second IEE/IEEE Int. Conf. on Genetic Algorithms in Engineering Systems: Innovations and Applications, 203–208.

  • Herrera, F. & Lozano, M. (1997c). Two-Loop Real-Coded Genetic Algorithms with Adaptive Control of Mutation Step Sizes. Technical Report#DECSAI970128, Dept. of Computer Science and Artificial Intelligence, University of Granada, Spain.

    Google Scholar 

  • Hinterding, R. (1995). Gaussian Mutation and Self-Adaption for Numeric Genetic Algorithms. Proc. of the International Conference on Evolutionary Computation, IEEE Press, 384–389.

  • Hinterding, R., Michalewicz, Z. & Peachey, T.C. (1996). Self-Adaptive Genetic Algorithm for Numeric Functions. 4th International Conference on Parallel Problem Solving from Nature, HM. Voigt, W. Ebeling, I. Rechenberg, HP. Schwefel (Eds.), LNCS 1141, Springer, 421–429.

  • Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. The University of Michigan Press.

  • Holland, J.H., Holyoak, K.J., Nisbett, R.E. & Thagard, P.R. (1986). Induction. Processes of Inference, Learning, and Discovery. The MIT Press, Cambridge.

    Google Scholar 

  • Iba, H., Akiba, S., Higuchi, T. & Sato, T. (1992). BUGS: A Bug-Based Search Strategy using Genetic Algorithms. Parallel Problem Solving from Nature 2, R. Männer and B. Manderick (Eds.) (Elsevier Science Publichers, Amsterdam), 165–174.

    Google Scholar 

  • Ichikawa, Y. & Ishii, Y. (1993). Retainig Diversity of Genetic Algorithms for Multivariable Optimization and Neural Network Learning. Proc. IEEE Int. Conf. on Neural Networks, San Francisco, California, 1110–1114.

  • Janikow, C.Z. & Michalewicz, Z. (1991). An Experimental Comparison of Binary and Floating Point Representation in Genetic Algorithms. Proc. of the Fourth Int. Conf. on Genetic Algorithms, R. Belew and L.B. Booker (Eds.) (Morgan Kaufmmann, San Mateo), 31–36.

    Google Scholar 

  • Karlin, S. (1968). Equilibrium Behavior of Population Genetic Models with Nonrandom mating. J. App. Prob. 5, 231–313.

    Google Scholar 

  • Kelly, J. & Davis, L. (1991). Hybridizing the GA and the K Nearest Neighbors Classification Algorithm. Proc. of the Fourth Int. Conf. on Genetic Algorithms, R. Belew and L.B. Booker (Ed.) (Morgan Kaufmmann, San Mateo), 377–383.

    Google Scholar 

  • Koza, J.R. (1992). Genetic Programming. The MIT press, Cambridge.

    Google Scholar 

  • Liepins, G.E. & Vose, M.D. (1990). Representational Issues in Genetic Optimization. J. Expt. Theor. Artif. Intell. 2, 101–115.

    Google Scholar 

  • Liepins, G.E. & Vose, M.D. (1992). Characterizing Crossover in Genetic Algorithms. Annals of Mathematics and Artificial Intelligence 5(1), 27–34.

    Google Scholar 

  • Lucasius, C. B. & Kateman G. (1989). Applications of genetic algorithms in chemometrics. Proc. of the Third International Conference on Genetic Algorithms, J. David Schaffer (Ed.) (Morgan Kaufmann Publishers, San Mateo), 170–176.

    Google Scholar 

  • Michalewicz, Z. (1991). A Genetic Algorithms for the Linear Transportation Problem. IEEE Trans. on Systems, Man, and Cybernetics 21(2), 445–452.

    Google Scholar 

  • Michalewicz, Z. (1992). Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, New York.

    Google Scholar 

  • Michielssen, E., Ranjithan, S. & Mittra, R. (1992). Optimal Multilayer Filter Design Using Real Coded Genetic Algorithms. IEE Proceedings-J, {vn139(6)}, 413–419.

  • Mizumoto M. (1989a). Pictorial Representations of fuzzy connectives, Part I: Cases of t-norms, t-conorms and averaging operators. Fuzzy Sets and Systems 31, 217–242.

    Google Scholar 

  • Mizumoto M. (1989b). Pictorial Representations of fuzzy connectives, Part II: Cases of Compensatory Operators and Seft-dual Operators. Fuzzy Sets and Systems 32, 45–79.

    Google Scholar 

  • Mühlenbein H. & Schlierkamp-Voosen D. (1993). Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization. Evolutionary Computation 1, 25–49.

    Google Scholar 

  • Mühlenbein, H. & Schlierkamp-Voosen, D. (1996a). Adaptation of Population Sizes by Competing Subpopulations. Proc. International Conference on Evolutionary Computation (ICEC’96), 330–335.

  • Mühlenbein, H., Bendisch, J. & Voigt, HM. (1996b). From Recombination of Genes to the Estimation of Distributions II. Continuous Parameters. 4th International Conference on Parallel Problem Solving from Nature, HM. Voigt, W. Ebeling, I. Rechenberg, HP. Schwefel (Eds.), LNCS 1141, Springer, 188–197.

  • Nomura, T. & Miyoshi, T. (1995). Numerical Coding and Unfair Average Crossover in GA for Fuzzy Clustering and their Applications for Automatic Fuzzy Rule Extraction. Proc. IEEE/Nagoya University WWW’95, 13–21.

  • Nomura, T. & Miyoshi, T. (1996). Numerical Coding and Unfair Average Crossover in GA for Fuzzy Rule Extraction in Dynamic Environments. Fuzzy Logic, Neural Networks, and Evolutionary Computation, T. Furuhashi and Y. Uchikawa (Eds.), Springer, 55–72.

  • Nomura, T. (1997a). An Analysis on Crossovers for Real Number Chromosomes in an Infinite Population Size. Proc. International Joint Conference on Artificial Intelligence (IJCAI’97), 936–941.

  • Nomura, T. (1997b). An Analysis on Linear Crossovers for Real Number Chromosomes in an Infinite Population Size. Proc. International Conference on Evolutionary Computation (ICEC’97), 111–114.

  • Ono, I. & Kobayashi S. (1997). A Real-Coded Genetic Algorithm for Function Optimization Using Unimodal Normal Distribution Crossover. Proc. of the Seventh Int. Conf. on Genetic Algorithms, T. Bäck (Ed.) (Morgan Kaufmann Publishers, San Francisco), 246–253.

    Google Scholar 

  • Qi, X. & Palmieri, F. (1994a). Theoretical Analysis of Evolutionary Algorithms With an Infinite Population Size in Continuous Space Part I: Basic Properties of Selection and Mutation. IEEE Trans. on Neural Networks 5(1), 102–119.

    Google Scholar 

  • Qi, X. & Palmieri, F. (1994b). Theoretical Analysis of Evolutionary Algorithms With an Infinite Population Size in Continuous Space Part II: Analysis of the Diversification Role of Crossover. IEEE Trans. on Neural Networks 5(1), 120–128.

    Google Scholar 

  • Radcliffe N.J. (1991a). Equivalence Class Analysis of Genetic Algorithms. Complex Systems {vn5(2)}, 183–205.

  • Radcliffe N.J. (1991b). Forma Analysis and Random Respecful Recombination. Proc. of the Fourth Int. Conf. on Genetic Algorithms, R. Belew and L.B. Booker (Eds.) (Morgan Kaufmmann, San Mateo), 222–229.

    Google Scholar 

  • Radcliffe N.J. (1992). Non-Linear Genetic Representations. Parallel Problem Solving from Nature 2, R. Männer and B. Manderick (Ed.) (Elsevier Science Publichers, Amsterdam), 259–268.

    Google Scholar 

  • Reeves, C.R. (1993). Using Genetic Algorithms with Small Populations. Proc. of the Fifth Int. Conf. on Genetic Algorithms, S. Forrest (Ed.) (Morgan Kaufmmann, San Mateo), 92–99.

    Google Scholar 

  • Renders, J. M. & Bersini, H. (1994). Hybridizing Genetic Algorithms with HillClimbing Methods for GlobalOptimization: Two PossibleWays. Proc. of The First IEEE Conference on Evolutionary Computation, 312–317.

  • Salomon, R. (1996a). The Influence of Different Coding Schemes on the Computational Complexity of Genetic Algorithms in Function Optimization. 4th International Conference on Parallel Problem Solving from Nature, HM. Voigt, W. Ebeling, I. Rechenberg, HP. Schwefel (Eds.), LNCS 1141, Springer, 227–235.

  • Salomon, R. (1996b). Some Comments on Evolutionary Algorithm Theory. Evolutionary Computation 4(4), 405–415.

    Google Scholar 

  • Schlierkamp-Voosen D. (1994). Strategy Adaptation by Competition. Proc. Second European Congress on Intelligent Techniques and Soft Computing, 1270–1274.

  • Schraudolph, N.N. & Belew, R.K. (1992). Dynamic Parameter Encoding for Genetic Algorithms. Machine Learning 9, 9–21.

    Google Scholar 

  • Schwefel, HP. (1995). Evolution and Optimum Seeking. Sixth-Generation Computer Technology Series. Wiley, New York.

    Google Scholar 

  • Sefrioui, M. & Périaux, J. (1996). Fast Convergence Thanks to Diversity. Proc. of the Fifth Annual Conference on Evolutionary Programming, 313–321.

  • Shaefer, C.G. (1987). The ARGOT Strategy: Adaptive Representation Genetic Optimizer Technique. Proc. Second Int. Conf. on Genetic Algorithms(L. Erlbaum Associates, Hillsdale, MA).

    Google Scholar 

  • Surry, P.D. & Radcliffe, N.J. (1996). Real Representations. Foundations of Genetic Algorithms 4, R.K. Belew, M.D. Vose (Eds.) (Morgan Kaufmann Publishers, San Francisco), 343–363.

    Google Scholar 

  • Syswerda, G. (1989). Uniform Crossover in Genetic Algorithms. Proc. of the Third Int. Conf. on Genetic Algorithms. Schaffer, J.D. (Ed.) (Morgan Kaufmann Publishers, San Mateo), 2–9.

    Google Scholar 

  • Syswerda, G. (1991). Schedule Optimization Using Genetic Algorithms. Handbook of Genetic Algorithms, L. Davis (Ed.) (Van Nostrand Reinhold, New York), 332–349.

    Google Scholar 

  • Tate, D.M. & Smith A.E. (1993). Expected Allele Coverage and the Role of Mutation in Genetic Algorithms. Proceeding of the Fifth International Conference on Genetic Algorithms, S. Forrest (Ed.) (Morgan Kaufmann Publishers, San Mateo), 31–36.

    Google Scholar 

  • Törn, A. & Antanas Ž. (1989). Global Optimization. Lecture Notes in Computer Science, Vol 350, Springer, Berlin.

    Google Scholar 

  • Tsutsui, S., Ghosh, A., Corne, D. & Fujimoto, Y. (1997). A Real Coded Genetic Algorithm with an Explorer and an Exploiter Population. Proc. of the Seventh Int. Conf. on Genetic Algorithms, T. Bäck (Ed.) (Morgan Kaufmann Publishers, San Francisco), 238–245.

    Google Scholar 

  • Voigt H.M. (1992). Fuzzy Evolutionary Algorithms. Technical Report tr92038, International Computer Science Institute (ICSI), Berkeley.

    Google Scholar 

  • Voigt H. M. (1993). A Multivalued Evolutionary Algorithm. Technical Report tr93022, International Computer Science Institute (ICSI), Berkeley.

    Google Scholar 

  • Voigt, H. M. & Anheyer, T. (1994). Modal Mutations in Evolutionary Algorithms. Proc. of The First IEEE Conference on Evolutionary Computation, 88–92.

  • Voigt, H.M., Mühlenbein, H. & Cvetković, D. (1995). Fuzzy Recombination for the Breeder Genetic Algorithm. Proc. of the Sixth Int. Conf. on Genetic Algorithms, L. Eshelman (Ed.) (Morgan Kaufmann Publishers, San Francisco), 104–111.

    Google Scholar 

  • Vose, M. D. (1991). Generalizing the Notion of Schemata in Genetic Algorithms. Artificial Intelligence 5, 385–396.

    Google Scholar 

  • Whitley, D., Starkweather, T. & Fuquay D. (1989). Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator. Proc. of the Third Int. Conf. on Genetic Algorithms, J. David Schaffer (Ed.) (Morgan Kaufmann Publishers, San Mateo), 133–140.

    Google Scholar 

  • Whitley, D., Mathias, K. & Fitzhorn, P. (1991). Delta Coding: An Iterative Search Strategy for Genetic Algorithms. Proc. of the Fourth Int. Conf. on Genetic Algorithms, R. Belew and L.B. Booker (Ed.) (Morgan Kaufmmann, San Mateo), 77–84.

    Google Scholar 

  • Whitley, L.D. & Schaffer, J.D. (1992). Proc. of the Int. Workshop on Combinations of Genetic Algorithms and Neural Network. IEEE Computer Society Press.

  • Wright, A. (1990). Genetic Algorithms for Real Parameter Optimization. Foundations of Genetic Algorithms, First Workshop on the Foundations of Genetic Algorithms and Classifier Systems, G.J.E. Rawlin (Ed.) (Morgan Kaufmann, Los Altos, CA), 205–218.

    Google Scholar 

  • Wright, A. (1991). Genetic Algorithms for Real Parameter Optimization. Foundations of Genetic Algorithms 1, G.J.E Rawlin (Ed.) (Morgan Kaufmann, San Mateo), 205–218.

  • Yang, JM. & Kao, C.Y. (1996). A Combined Evolutionary Algorithm for Real Parameter Optimization. Proc. 1996 IEEE Int. Conf. on Evolutionary Computation, IEEE Service Center, Piscataway, 732–737.

    Google Scholar 

  • Yang, J-M. & Horng, J-T. & Kao, C.Y. (1997). A Continuous Genetic Algorithm for Global Optimization. Proc. of the Seventh Int. Conf. on Genetic Algorithms, T. Bäck (Ed.) (Morgan Kaufmann Publishers, San Francisco), 230–237.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, Avda. Andalucia 38, 18071, Granada, Spain

    F. Herrera, M. Lozano & J.L. Verdegay

Authors
  1. F. Herrera
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Lozano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J.L. Verdegay
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Herrera, F., Lozano, M. & Verdegay, J. Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis. Artificial Intelligence Review 12, 265–319 (1998). https://doi.org/10.1023/A:1006504901164

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1006504901164

Search

Navigation