article

Real-coded memetic algorithms with crossover hill-climbing

Authors:

Manuel Lozano,

Francisco Herrera,

Natalio Krasnogor,

Daniel MolinaAuthors Info & Claims

Evolutionary Computation, Volume 12, Issue 3

Pages 273 - 302

https://doi.org/10.1162/1063656041774983

Published: 01 September 2004 Publication History

Get Access

Abstract

This paper presents a real-coded memetic algorithm that applies a crossover hill-climbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hill-climbing exploits the self-adaptive capacity of real-parameter crossover operators with the aim of producing an effective local tuning on the solutions (accuracy). An important aspect of the memetic algorithm proposed is that it adaptively assigns different local search probabilities to individuals. It was observed that the algorithm adjusts the global/local search balance according to the particularities of each problem instance. Experimental results show that, for a wide range of problems, the method we propose here consistently outperforms other real-coded memetic algorithms which appeared in the literature.

References

[1]

Angeline, P.J. (1998). Using selection to improve particle swarm optimization. In Proc. of the International Congress on Evolutionary Computation 1998, pages 84-89.]]

Crossref

Google Scholar

[2]

Bäck, T. (1992). Self-adaptation in genetic algorithms. In Varela, F.J. and Bourgine, P., editors, Proc. of the First European Conference on Artificial Life, pages 263-271, MIT Press, Cambridge, MA.]]

Google Scholar

[3]

Bäck, T., Schütz, M. and Khuri, S. (1996). Evolution strategies: an alternative evolution computation method. In Alliot, J.-M., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D., Editors, Artificial Evolution, pages 3-20, Springer, Berlin.]]

Digital Library

Google Scholar

[4]

Beyer, H.-G. and Deb, K. (2001). On self-adaptive features in real-parameter evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 5(3): 250-270.]]

Digital Library

Google Scholar

[5]

Craighurst, R. and Martin, W. (1995). Enhancing GA performance through crossover prohibitions based on ancestry. In Eshelman, L.J., editor, Proc. 6th Int. Conf. Genetic Algorithms, pages 130-137, Morgan Kaufmann, San Mateo, California.]]

Digital Library

Google Scholar

[6]

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

Google Scholar

[7]

Deb, K. (2001). Multi-Objective Optimization using Evolutionary Algorithms, John Wiley & Sons, New York.]]

Digital Library

Google Scholar

[8]

Deb, K. and Beyer, H. (2001). Self-adaptive genetic algorithms with simulated binary crossover. Evolutionary Computation Journal, 9(2): 195-219.]]

Digital Library

Google Scholar

[9]

Deb, K., Anand, A. and Joshi, D. (2002). A computationally efficient evolutionary algorithm for real-parameter evolution. Evolutionary Computation Journal, 10(4): 371-395.]]

Digital Library

Google Scholar

[10]

De Jong, K.A. (1975). An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Doctoral dissertation, University of Michigan, Ann Arbor. Dissertation Abstracts International, 36(10), 5140B (University Microfilms No 76-9381).]]

Digital Library

Google Scholar

[11]

De Jong, K.A. and Spears, W.M. (1992). A formal analysis of the role of multi-point crossover in genetic algorithms. Annals of Mathematics and Artificial Intelligence, 5(1): 1-26.]]

Crossref

Google Scholar

[12]

Dietzfelbinger, M., Naudts, B., Van Hoyweghen, C. and Wegener, I. (2003). The Analysis of a Recombinative Hill-Climber on H-IFF. IEEE Transactions on Evolutionary Computation, 7(5): 417-423.]]

Digital Library

Google Scholar

[13]

Eiben, A.E. and Bäck, T. (1997). Empirical investigation of multiparent recombination operators in evolution strategies. Evolutionary Computation, 5(3): 347-365.]]

Digital Library

Google Scholar

[14]

Eshelman, L.J. (1991). The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination. In Rawlin, G.J.E., editor, Foundations of Genetic Algorithms 1, pages 265-283, Morgan Kaufmann, San Mateo, California.]]

Google Scholar

[15]

Eshelman, L.J. and Schaffer, J.D. (1991). Preventing premature convergence in genetic algorithms by preventing incest. In Belew, R. and Booker, L.B., editors, Proc. of the Fourth Int. Conf. on Genetic Algorithms, pages 115-122, Morgan Kaufmann, San Mateo, California.]]

Google Scholar

[16]

Eshelman, L.J. and Schaffer, J.D. (1993). Real-coded genetic algorithms and interval-schemata. In Whitley, L.D., editor, Foundations of Genetic Algorithms 2, pages 187-202, Morgan Kaufmann, San Mateo, California.]]

Google Scholar

[17]

Eshelman, L.J. Mathias, K.E. and Schaffer, J.D. (1997). Convergence controlled variation. In Belew, R. and Vose, M., editors, Foundations of Genetic Algorithms 4, pages 203-224, Morgan Kaufmann, San Mateo, California.]]

Google Scholar

[18]

Espinoza, F.P., Minsker, B.S. and Goldberg, D.E. (2001). A self adaptive hybrid genetic algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference 2001, pages 759, Morgan Kaufmann, San Mateo, California.]]

Google Scholar

[19]

Fernandes, C. and Rosa, A. (2001). A Study on non-random mating and varying population size in genetic algorithms using a royal road function. In Proc. of the 2001 Congress on Evolutionary Computation, pages 60-66, IEEE Press, Piscataway, New Jersey.]]

Crossref

Google Scholar

[20]

Fogel, D.B. and Beyer, H.-G. (1995). A note on the empirical evaluation of intermediate recombination. Evolutionary Computation, 3(4): 491-495.]]

Digital Library

Google Scholar

[21]

Gehlhaar, D. and Fogel, D. (1996). Tuning evolutionary programming for conformationally flexible molecular docking. In Fogel, L., Angeline, P. and Bäck, T., editors, Proc. Of the Fifth Annual Conference on Evolutionary Programming, pages 419-429, MIT Press, Cambridge, MA.]]

Google Scholar

[22]

Goldberg, D.E. and Deb, K. (1991). A comparative analysis of selection schemes used in genetic algorithms. In Rawlins, G.J.E., editor, Foundations of Genetic Algorithms, pages 69-93, Morgan Kaufmann, San Mateo, California.]]

Google Scholar

[23]

Goldberg, D.E. and Wang, L. (1997). Adaptive niching via coevolutionary sharing. In Quagliarella et al., editors, Genetic Algorithms in Engineering and Computer Science, pages 21-38, John Wiley and Sons, New York.]]

Google Scholar

[24]

Goldberg, D.E. and Voessner, S. (1999). Optimizing global-local search hybrids. In Banzhaf, W., et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference'99, pages 220-228, Morgan Kaufmann, San Mateo, California.]]

Google Scholar

[25]

Griewangk, A.O. (1981). Generalized descent of global optimization. Journal of Optimization Theory and Applications, 34: 11-39.]]

Crossref

Google Scholar

[26]

Hart, W.E. (1994). Adaptive Global Optimization with Local Search. PhD Thesis, University of California, San Diego, California.]]

Digital Library

Google Scholar

[27]

Hart, W.E., Rosin, C.R., Belew, R.K. and Morris, G.M. (2000). Improved evolutionary hybrids for flexible ligand docking in autodock. In Proc Intl Conf on Optimization in Computational Chem and Molec Bio., pages 209-230.]]

Crossref

Google Scholar

[28]

Hart, W.E. (2001a). A convergence analysis of unconstrained and bound constrained evolutionary pattern search. Evolutionary Computation, 9(1): 1-23.]]

Digital Library

Google Scholar

[29]

Hart, W.E. (2001b). Evolutionary pattern search algorithms for unconstrained and linearly constrained optimization. IEEE trans. Evolutionary Computation, 5(4): 388-397.]]

Digital Library

Google Scholar

[30]

Herrera, F., Lozano, M. and Verdegay, J.L. (1996). 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.]]

Crossref

Google Scholar

[31]

Herrera, F., Lozano, M. and Verdegay, J.L. (1998). Tackling real-coded genetic algorithms: operators and tools for the behavioral analysis. Artificial Intelligence Reviews, 12(4): 265-319.]]

Digital Library

Google Scholar

[32]

Herrera, F., Lozano, M. (2000a). Two-loop real-coded genetic algorithms with adaptive control of mutation step sizes. Applied Intelligence, 13: 187-204.]]

Digital Library

Google Scholar

[33]

Herrera, F. and Lozano, M. (2000b). Gradual distributed real-coded genetic algorithms. IEEE Transactions on Evolutionary Computation 4(1): 43-63.]]

Digital Library

Google Scholar

[34]

Herrera, F., Lozano, M., Pérez, E., Sánchez, A. M. and Villar, P. (2002). Multiple crossover per couple with selection of the two best offspring: an experimental study with the BLX-α crossover operator for real-coded genetic algorithms. In Garijo, F.J., Riquelme, and J.C., Toro, M., editors, IBERAMIA 2002, Lecture Notes in Artificial Intelligence 2527, pages 392-401, Springer-Verlag, Berlin.]]

Digital Library

Google Scholar

[35]

Herrera, F., Lozano, M. and Sánchez A.M. (2003). A taxonomy for the crossover operator for realcoded genetic algorithms. An Experimental Study. International Journal of Intelligent Systems, 18(3): 309-338.]]

Crossref

Google Scholar

[36]

Houck, C.R., Joines, J.A., Kay, M.G. and Wilson, J.R. (1997). Empirical investigation of the benefits of partial lamarckianism. Evolutionary Computation Journal, 5(1): 31-60.]]

Digital Library

Google Scholar

[37]

Huang, C.-F. (2001). An analysis of mate selection in genetic algorithms. In Proc. of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 766, Morgan Kaufmann, San Mateo, California.]]

Google Scholar

[38]

Joines, J.A. and Kay, G.M. (2002). Hybrid genetic algorithms and random linkage. In Proceedings of the 2002 Congress on Evolutionary Computation, pages 1733-1738, IEEE Press, Piscataway, New Jersey.]]

Digital Library

Google Scholar

[39]

Jones, T. (1995). Crossover, macromutation, and population-based search. In Eshelman, L., editor, Proc. of the Sixth Int. Conf. on Genetic Algorithms, pages 73-80, Morgan Kaufmann, San Francisco, California.]]

Digital Library

Google Scholar

[40]

Kazarlis, S.A., Papadakis, S.E., Theocharis, J.B. and Petridis, V. (2001). Microgenetic algorithms as generalized hill-climbing operators for GA Optimization. IEEE Transaction on Evolutionary Computation, 5(3): 204-217.]]

Digital Library

Google Scholar

[41]

Kemenade, C.H.M. (1996). Cluster evolution strategies, enhancing the sampling density function using representatives. In Proceedings of the Third IEEE conference on Evolutionary Computation, pages 637-642. IEEE Press, Piscataway, New Jersey.]]

Crossref

Google Scholar

[42]

Kita, H., Ono, I. and Kobayashi, S. (1999). Multi-parental extension of the unimodal normal distribution crossover for real-coded genetic algorithms. In Proc. of the International Conference on Evolutionary Computation'99, pages 646-651, IEEE Press, Piscataway, New Jersey.]]

Crossref

Google Scholar

[43]

Kita, H. (2001). A comparison study of self-adaptation in evolution strategies and real-coded genetic algorithms. Evolutionary Computation Journal, 9(2): 223-241.]]

Digital Library

Google Scholar

[44]

Krasnogor, N. (2002). Studies on the Theory and Design Space of Memetic Algorithms. PhD Thesis, University of the West of England, Bristol, United Kingdom.]]

Google Scholar

[45]

Krasnogor, N. and Smith, J.E. (2000). A Memetic Algorithm with Self-Adapting Local Search: TSP as a case study. Proceedings of the 2000 International Conference on Genetic and Evolutionary Computation, pages 987-994, Morgan Kaufmann, San Mateo, California.]]

Google Scholar

[46]

Krasnogor, N. and Smith J.E. (2001). Emergence of Profitable Search Strategies Based on a Simple Inheritance Mechanism. In Proceedings of the 2001 International Conference on Genetic and Evolutionary Computation, pages p. 432-439, Morgan Kaufmann, San Mateo, California.]]

Google Scholar

[47]

Land, M.W.S. (1998). Evolutionary Algorithms with Local Search for Combinatorial Optimisation. PhD Thesis, University of California, San Diego.]]

Digital Library

Google Scholar

[48]

Magyar, G., Johnsson, M. and Nevalainen, O. (2000). An adaptive hybrid genetic algorithm for the three-matching problem. IEEE Transactions on Evolutionary Computation, 4(2): 135-146.]]

Digital Library

Google Scholar

[49]

Merz, P. (2000). Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and Effective Search Strategies. PhD Thesis, University of Siegen, Germany.]]

Google Scholar

[50]

Moscato, P.A. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Technical Report Caltech Concurrent Computation Program Report 826, Caltech, Caltech, Pasadena, California.]]

Google Scholar

[51]

Moscato, P.A. (1999). Memetic algorithms: a short introduction. In Corne, D., Dorigo, M., and Glower, F., editors, New Ideas in Optimization, pages 219-234, McGraw-Hill, London.]]

Digital Library

Google Scholar

[52]

Mühlenbein, H., Schomisch, M. and Born, J. (1991). The parallel genetic algorithm as function optimizer. In Belew, R., Booker, L.B., editors, Fourth Int. Conf. on Genetic Algorithms, pages 271-278, Morgan Kaufmann, San Mateo, California.]]

Google Scholar

[53]

Mühlenbein, H. and Schlierkamp-Voosen, D. (1993). Predictive models for the breeder genetic algorithm I. continuous parameter optimization. Evolutionary Computation, 1: 25-49.]]

Digital Library

Google Scholar

[54]

Nagata, Y. and Kobayashi, S. (1997). Edge assembly crossover: a high-power genetic algorithm for the traveling salesman problem. In Bäck, T., editor, Proc. of the Seventh Int. Conf. on Genetic Algorithms, pages 450-457, Morgan Kaufmmann, San Mateo, California.]]

Google Scholar

[55]

O'Reilly, U.-M. and Oppacher F. (1995). Hybridized crossover-based search techniques for program discovery. In IEEE International Conference on Evolutionary Computation 1995, pages 573-578, IEEE Press, Piscataway, New Jersey.]]

Crossref

Google Scholar

[56]

Parthasarathy, P.V., Goldberg, D.E. and Burns, S.A. (2001). Tackling multimodal problems in hybrid genetic algorithms. In Whitley, D., editor, Proc. of the Genetic and Evolutionary Computation Conference 2001, pages 775, Morgan Kaufmann, San Francisco, California.]]

Google Scholar

[57]

Rechenberg, I. (1975). Evolutionsstrategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution. Frommann-Holzboog.]]

Google Scholar

[58]

Renders, J.M. and Bersini, H. (1994). Hybridizing genetic algorithms with hill-climbing methods for global optimization: two possible ways. In Proc. of The First IEEE Conference on Evolutionary Computation, pages 312-317, IEEE Press, Piscataway, New Jersey.]]

Crossref

Google Scholar

[59]

Renders J.M. and Flasse, S.P. (1996). Hybrid methods using genetic algorithms for global optimization. IEEE Transactions on Systems, Man, and Cybernetics, 26(2): 243-258.]]

Digital Library

Google Scholar

[60]

Ronald, E. (1993). When selection meets seduction. In Forrest, S., editor, Proc. of the Fifth Int. Conf. on Genetic Algorithms, pages 167-173, Morgan Kaufmann, San Mateo, California.]]

Digital Library

Google Scholar

[61]

Rosin, C.D., Halliday, R.S., Hart, W.E. and Belew, R.K. (1997). A comparison of global and local search methods in drug docking. In Bäck, T., editor, Proc. of the Seventh Int. Conf. on Genetic Algorithms, pages 221-228, Morgan Kaufmmann, San Mateo, California.]]

Google Scholar

[62]

Salomon, R. (1998). Evolutionary algorithms and gradient search: Similarities and differences. IEEE Transactions on Evolutionary Computation, 2(2): 45-55.]]

Digital Library

Google Scholar

[63]

Satoh, H. Yamamura, M. and Kobayashi, S. (1996). Minimal generation gap model for GAs considering both exploration and exploitation. In IIZUKA'96, pages 494-497.]]

Google Scholar

[64]

Schaffer D., Mani M., Eshelman L. and Mathias K. (1999). The Effect of Incest Prevention on Genetic Drift. In Banzhaf, W. and Reeves, C., editors, Foundations of Genetic Algorithms 5, pages 235-243, Morgan Kaufmann, San Mateo, California.]]

Google Scholar

[65]

Schlierkamp-Voosen, D. and Mühlenbein, H. (1994). Strategy adaptation by competing subpopulations. In Davidor, Y., Schwefel, H.-P., Männer, and R., editors, Parallel Problem Solving from Nature 3, pages 199-208, Springer-Verlag, Berlin, Germany.]]

Digital Library

Google Scholar

[66]

Schwefel, H.-P. (1981). Numerical Optimization of Computer Models, Wiley, Chichester.]]

Digital Library

Google Scholar

[67]

Seront, G. and Bersini, H. (2000). A new GA-local search hybrid for continuous optimization based on multi level single linkage clustering. In Whitley, D., editor, Proc. of the Genetic and Evolutionary Computation Conference 2000, pages 90-95, Morgan Kaufmann, San Francisco, California.]]

Google Scholar

[68]

Shimodaira, H. (1996). A new genetic algorithm using large mutation rates and population-elitist selection (GALME). In Proc. of the International Conference on Tools with Artificial Intelligence, pages 25-32, IEEE Computer Society.]]

Digital Library

Google Scholar

[69]

Smith, J.E. and Fogarty, T.C. (1997). Operator and parameter adaptation in genetic algorithms. Soft Computing, 1(2): 81-87.]]

Crossref

Google Scholar

[70]

Smith, J.E. (2001). Modelling GAs with Self-Adapting Mutation Rates. Proc. of the Genetic and Evolutionary Computation Conference 2001.]]

Google Scholar

[71]

Solis, F.J. and Wets, R.J.-B. (1981). Minimization by random search techniques. Mathematical Operations Research, 6: 19-30.]]

Digital Library

Google Scholar

[72]

Spears, W.M. (1993). Crossover or mutation?. In Whitley, L.D., editor, Foundations of Genetic Algorithms 2, pages 221-238, Morgan Kaufmann, San Mateo, California.]]

Google Scholar

[73]

Spears, W.M. (1995). Adapting crossover in evolutionary algorithms. In Proc. of the Evolutionary Programming Conference 1995, pages 367-384.]]

Google Scholar

[74]

Storn, R. and Price, K. (1995). Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, International Computer Science Institute, Berkeley, California.]]

Google Scholar

[75]

Törn, A. and Antanas, Ž. (1989). Global Optimization, Lecture Notes in Computer Science, 350, Springer, Berlin, Germany.]]

Google Scholar

[76]

Tsutsui, S. and Fujimoto, Y. (1993). Forking genetic algorithm with blocking and shrinking modes. In Forrest, S., editor, Proc. of the Fifth Int. Conf. on Genetic Algorithms, pages 206-213, Morgan Kaufmann, San Mateo, California.]]

Digital Library

Google Scholar

[77]

Tsutsui, S., Yamamura, M. and Higuchi, T. (1999). Multi-parent recombination with simplex crossover in real-coded genetic algorithms. In Proc. of the Genetic and Evolutionary Computation Conference (GECCO-99), 657-664, Morgan Kaufmann, San Mateo, California.]]

Google Scholar

[78]

Walters, T. (1998). Repair and brood selection in the traveling salesman problem. In Eiben, A. E., Bäch, T., Schoenauer, M. and Schwefel H.-P., editors, Proceedings of the Fifth International Conference on Parallel Problem Solving from Nature, pages 813-822, Springer-Verlag, Berlin, Germany.]]

Digital Library

Google Scholar

[79]

Whitley, D., Rana, S., Dzubera, J. and Mathias, E. (1996). Evaluating evolutionary algorithms. Artificial Intelligence, 85: 245-276.]]

Digital Library

Google Scholar

[80]

Yang, J.-M. and Kao, C.-Y. (2000). Integrating adaptive mutations and family competition into genetic algorithms as function optimiser. Soft Computing, 4: 89-102.]]

Crossref

Google Scholar

[81]

Yoon, H.-S. and Moon, B.-R. (2002). An empirical study on the synergy of multiple crossover operators. IEEE Transactions on Evolutionary Computation, 6(2): 212-223.]]

Digital Library

Google Scholar

[82]

Zhang, C.-K. and Shao, H.-H. (2001). A hybrid strategy: real-coded genetic algorithm and chaotic search. In Proc. IEEE International Conference on Systems, Man, and Cybernetics 2001, pages 2361-2364.]]

Google Scholar

Cited By

View all

Biedma-Rdguez CGacto MAnguita-Ruiz AAlcalá RAguilera CAlcalá-Fdez J(2023)Learning positive-negative rule-based fuzzy associative classifiers with a good trade-off between complexity and accuracyFuzzy Sets and Systems10.1016/j.fss.2023.03.014465:COnline publication date: 15-Aug-2023
https://dl.acm.org/doi/10.1016/j.fss.2023.03.014
Hedar ADeabes WAmin HAlmaraashi MFukushima M(2022)Global sensing search for nonlinear global optimizationJournal of Global Optimization10.1007/s10898-021-01075-282:4(753-802)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s10898-021-01075-2
Qin ZYe ZWang ZGan HWang F(2021)A Multimodal Biomedical Image Registration Method Based on an Improved Genetic Algorithm Inspired by Hybrid Breeding2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC52423.2021.9658798(1272-1279)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1109/SMC52423.2021.9658798
Show More Cited By

Index Terms

Real-coded memetic algorithms with crossover hill-climbing
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Data structures design and analysis
      1. Sorting and searching
    2. Mathematical optimization

Recommendations

Improved Real-coding Genetic Algorithm
NSWCTC '09: Proceedings of the 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing - Volume 02

To solve the slow convergence rate and local convergence of Simple Genetic Algorithm, an improved genetic algorithm (IGA) with real-coding, elite reservation, 2/4competitive choosing and adaptive genetic strategy is proposed. The experiment shows that ...
Gradual distributed real-coded genetic algorithms

A major problem in the use of genetic algorithms is premature convergence. One approach for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, several subpopulations that are processed by ...
An improved class of real-coded Genetic Algorithms for numerical optimization

Over the last few decades, many improved Evolutionary Algorithms (EAs) have been proposed to tackle different types of optimization problems. Genetic Algorithm (GA) among other canonical algorithms have not shown consistent performance over a range of ...

Reviews

Reviewer: Chenyi Hu

A major part of evolutionary computing is developing genetic algorithms to find solutions to computing problems. The authors of this paper propose a crossover operator, which extends previous results, on two chromosomes coded with real intervals to solve problems with a continuous mathematical model. The operator generates offspring that are parent-centric and good for local search strategies. After reviewing some real-coded crossover algorithms, the authors propose a real-coded memetic algorithm (RCMA) that invokes real-parameter crossover hill-climbing (XHC). Two important factors of the RCMA are the population diversity by means of the negative assortative mating strategy and the refinement of solutions carried out by XHC. The authors also report their test results on six classic nonlinear continuous functions and three recent application problems. These testing results are interesting; however, they do not show any expected performance advantages. On our planet, some species flourish, while others disappear. Nowadays, we can only see the skeletons of dinosaurs in museums. Will evolutionary computing, or more specifically RCMA, prove to be a flourishing species or a dinosaur skeleton__?__ Only time will tell. Online Computing Reviews Service

Access critical reviews of Computing literature here

Become a reviewer for Computing Reviews.

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

Evolutionary Computation Volume 12, Issue 3

Special issue on magnetic algorithms

September 2004

129 pages

ISSN:1063-6560

EISSN:1530-9304

Issue’s Table of Contents

Publisher

MIT Press

Cambridge, MA, United States

Publication History

Published: 01 September 2004

Published in EVOL Volume 12, Issue 3

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

68
Total Citations
View Citations
315
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 19 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Biedma-Rdguez CGacto MAnguita-Ruiz AAlcalá RAguilera CAlcalá-Fdez J(2023)Learning positive-negative rule-based fuzzy associative classifiers with a good trade-off between complexity and accuracyFuzzy Sets and Systems10.1016/j.fss.2023.03.014465:COnline publication date: 15-Aug-2023
https://dl.acm.org/doi/10.1016/j.fss.2023.03.014
Hedar ADeabes WAmin HAlmaraashi MFukushima M(2022)Global sensing search for nonlinear global optimizationJournal of Global Optimization10.1007/s10898-021-01075-282:4(753-802)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s10898-021-01075-2
Qin ZYe ZWang ZGan HWang F(2021)A Multimodal Biomedical Image Registration Method Based on an Improved Genetic Algorithm Inspired by Hybrid Breeding2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC52423.2021.9658798(1272-1279)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1109/SMC52423.2021.9658798
Alvarado JVelasco JChávez FHidalgo Jde Vega F(2021)Blood Glucose Prediction Using a Two Phase TSK Fuzzy Rule Based System2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504992(712-719)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1109/CEC45853.2021.9504992
Rahman RHasan SRahman RJahan KIslam SShadman ATowqir SAlam K(2019)A Novel Fuzzy Inspired Bat Algorithm for Multidimensional Function Optimization ProblemInternational Journal of Fuzzy System Applications10.4018/IJFSA.20190101058:1(83-100)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.4018/IJFSA.2019010105
Alarifi AAlzubi A(2018)Memetic Search Optimization Along with Genetic Scale Recurrent Neural Network for Predictive Rate of Implant TreatmentJournal of Medical Systems10.1007/s10916-018-1051-142:11(1-7)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1007/s10916-018-1051-1
Bermejo EChica MDamas SSalcedo-Sanz SCordón O(2018)Mono-modal Medical Image Registration with Coral Reef OptimizationHybrid Artificial Intelligent Systems10.1007/978-3-319-92639-1_19(222-234)Online publication date: 20-Jun-2018
https://dl.acm.org/doi/10.1007/978-3-319-92639-1_19
Bermejo EChica MSanz SCordón O(2017)Coral Reef Optimization for intensity-based medical image registration2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969357(533-540)Online publication date: 5-Jun-2017
https://dl.acm.org/doi/10.1109/CEC.2017.7969357
Umbarkar AJoshi MHong W(2016)Comparative study of diversity based parallel dual population genetic algorithm for unconstrained function optimisationsInternational Journal of Bio-Inspired Computation10.5555/2995373.29953818:4(248-263)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.5555/2995373.2995381
Martín DAlcalá-Fdez JRosete AHerrera F(2016)NICGARInformation Sciences: an International Journal10.1016/j.ins.2016.03.039355:C(208-228)Online publication date: 10-Aug-2016
https://dl.acm.org/doi/10.1016/j.ins.2016.03.039
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Improved Real-coding Genetic Algorithm

Gradual distributed real-coded genetic algorithms

An improved class of real-coded Genetic Algorithms for numerical optimization

Reviews

Access critical reviews of Computing literature here