Skip to main content
SpringerLink
Log in

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

  • Chapter
Hybrid Evolutionary Algorithms

Part of the book series: Studies in Computational Intelligence ((SCI,volume 75))

Evolutionary computation has become an important problem solving methodology among many researchers. The population-based collective learning process, selfadaptation, and robustness are some of the key features of evolutionary algorithms when compared to other global optimization techniques. Even though evolutionary computation has been widely accepted for solving several important practical applications in engineering, business, commerce, etc., yet in practice sometimes they deliver only marginal performance. Inappropriate selection of various parameters, representation, etc. are frequently blamed. There is little reason to expect that one can find a uniformly best algorithm for solving all optimization problems. This is in accordance with the No Free Lunch theorem, which explains that for any algorithm, any elevated performance over one class of problems is exactly paid for in performance over another class. Evolutionary algorithm behavior is determined by the exploitation and exploration relationship kept throughout the run. All these clearly illustrates the need for hybrid evolutionary approaches where the main task is to optimize the performance of the direct evolutionary approach. Recently, hybridization of evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty, and vagueness. In this chapter, first we emphasize the need for hybrid evolutionary algorithms and then we illustrate the various possibilities for hybridization of an evolutionary algorithm and also present some of the generic hybrid evolutionary architectures that has evolved during the last couple of decades. We also provide a review of some of the interesting hybrid frameworks reported in the literature.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abraham A (2004) Meta-Learning Evolutionary Artificial Neural Networks, Neurocom-puting Journal, Elsevier Science, Netherlands, 56c, pp. 1-38

    Google Scholar 

  2. Ahuja RK, Orlin JB, and Tiwari A (2000) A greedy genetic algorithm for the quadratic assignment problem, Computers and Operations Research, 27, 917-934

    Article  MATH  MathSciNet  Google Scholar 

  3. Aruldoss AVT and Ebenezer JA (2004) Hybrid PSO-SQP for economic dispatch with valve-point effect, Electric Power Systems Research, 71(1), pp. 51-59

    Google Scholar 

  4. Aruldoss AVT and Ebenezer JA (2005) A modified hybrid EP-SQP approach for dynamic dispatch with valve-point effect, International Journal of Electrical Power and Energy Systems, 27(8), pp. 594-601

    Google Scholar 

  5. Attaviriyanupap KH, Tanaka E, and Hasegawa J (2002) A hybrid EP and SQP for dynamic economic dispatch with nonsmooth incremental fuel cost function, IEEE Transaction on Power Systems, 17(2), pp. 411-416

    Article  Google Scholar 

  6. Burke EK and Smith AJ (2000) Hybrid evolutionary techniques for the maintenance scheduling problem, IEEE Transactions on Power Systems, 1(1), pp. 122-128

    Article  Google Scholar 

  7. Clerc M and Kennedy J (2002) The particle swarm: explosion stability and convergence in a multi-dimensional complex space, IEEE Transaction on Evolutionary Computation 6(1), pp. 58-73

    Article  Google Scholar 

  8. Dozier G, Bowen J, and Homaifar A (1998) Solving constraint satisfaction problems using hybrid evolutionary search, IEEE Transactions on Evolutionary Computation, 2(1), pp. 23-33

    Article  Google Scholar 

  9. Eberhart RC and Kennedy J (1995) A new optimizer using particle swarm theory, In Pro-ceedings of 6th Internationl Symposium on Micro Machine and Human Science, Nagoya, Japan, IEEE Service Center, Piscataway, NJ, pp. 39-43

    Google Scholar 

  10. Estudillo ACM, Martínez CH, Estudillo FJM, and Pedrajas GC (2006) Hybridization of evolutionary algorithms and local search by means of a clustering method, IEEE Transactions on Systems, Man and Cybernetics, Part B, 36(3), pp. 534-545

    Google Scholar 

  11. Fatourechi M, Bashashati A, Ward RK, and Birch G (2005) A hybrid genetic algorithm approach for improving the performance of the LF-ASD brain computer interface, In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP05), Philadelphia, pp. 345-348

    Google Scholar 

  12. Fleurent C and Ferland J (1994) Genetic hybrids for the quadratic assignment problems, In Quadratic Assignment and Related Problems, Pardalos PM and Wolkowicz H (Eds.), DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol. 16, AMS: Providence, Rhode Island, pp. 190-206

    Google Scholar 

  13. Fogel DB (1991) System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling, Ginn & Co., Needham, MA

    Google Scholar 

  14. Fogel DB (1994) An introduction to simulated evolutionary optimization. IEEE Transaction on Neural Networks, 5(1), pp. 3-14

    Article  Google Scholar 

  15. Fogel LJ, Owens AJ, and Walsh MJ (1966) Artificial Intelligence Through Simulated Evolution, Wiley, USA

    MATH  Google Scholar 

  16. Ganesh K and Punniyamoorthy M (2004) Optimization of continuous-time production planning using hybrid genetic algorithms-simulated annealing, International Journal of Advanced Manufacturing Technology, 26(1), pp. 148-154

    Google Scholar 

  17. Grefenstette JJ (1987) Incorporating problem specific knowledge into genetic algorithms, In Davis L. (Ed.), Genetic Algorithms and Simulated Annealing, Morgan Kaufmann, Los Altos, CA, pp. 42-60

    Google Scholar 

  18. Grimaldi EA, Grimacia F, Mussetta M, Pirinoli P, and Zich RE (2004) A new hybrid genetical - swarm algorithm for electromagnetic optimization, In Proceedings of International Conference on Computational Electromagnetics and its Applications, Beijing, China, pp. 157-160

    Google Scholar 

  19. Grosan C, Abraham A, and Nicoara M (2005) Search optimization using hybrid particle sub-swarms and evolutionary algorithms, International Journal of Simulation Systems, Science and Technology, UK, 6(10-11), pp. 60-79

    Google Scholar 

  20. Harik GR and Goldberg DE (2000) Linkage learning through probabilistic expression. Computer Methods in Applied Mechanics and Engineering 186(2-4), pp. 295-310

    Article  MATH  Google Scholar 

  21. Hart WE, Krasnogor N, and Smith JE (Eds.) (2005) Recent Advances in Memetic Algorithms, Series: Studies in Fuzziness and Soft Computing, Vol. 166

    Google Scholar 

  22. Herrera F and Lozano M (1996) Adaptation of genetic algorithm parameters based on Fuzzy logic controllers. Genetic Algorithms and Soft Computing, Herrera F, Verdegay JL (Eds.), pp. 95-125

    Google Scholar 

  23. Holland JH (1975) Adaptation in natural and artificial systems, The University of Michigan Press, Ann Arbor, MI

    Google Scholar 

  24. Hopper E and Turton BCH (2001) An empirical investigation of metaheuristic and heuristic algorithms for a 2D packing problem, European Journal of Operational Research 128, pp. 34-57

    Article  MATH  Google Scholar 

  25. Jeong SJ, Lim SJ, and Kim KS (2005) Hybrid approach to production scheduling using genetic algorithm and simulation, International Journal of Advanced Manufacturing Technology, 28(1), pp. 129-136

    Google Scholar 

  26. Keedwell E and Khu ST (2005) A hybrid genetic algorithm for the design of water distri-bution networks, Engineering Applications of Artificial Intelligence, 18(4), pp. 461-472

    Article  Google Scholar 

  27. Kennedy J and Eberhart RC (1995) Particle swarm optimization, In Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942-1948

    Chapter  Google Scholar 

  28. Kennedy J (1997) The particle swarm: social adaptation of knowledge, In Proceedings of IEEE International Conference on Evolutionary Computation, Indianapolis, IN, 1997, pp. 303-308

    Google Scholar 

  29. Kim DH and Cho JH (2005) Robust tuning of PID controller using bacterial-foraging-based optimization, JACIII 9(6), 669-676

    MathSciNet  Google Scholar 

  30. Kim JH, Myung H, and Jeon JY (1995) Hybrid evolutionary programming with fast convergence for constrained optimization problems, IEEE Conference on Intelligent Systems for the 21 Century, Vancouver, Canada, pp. 3047-3052

    Google Scholar 

  31. Koza JR (1992) Genetic Programming, MIT Press, Cambridge, MA

    MATH  Google Scholar 

  32. Koza JR (1992) Genetic Programming: On the Programming of Computers By Means of Natural Selection, MIT Press, Cambridge, MA

    MATH  Google Scholar 

  33. Koza JR (1994) Genetic Programming II: Automatic Discovery of Reusable Programs, MIT Press, Cambridge, MA

    MATH  Google Scholar 

  34. Koza JR, Bennett FH, Andre D, and Keane MA (1999) Genetic programming III: Dar-winian invention and problem solving, Morgan Kaufmann, Los Altos, CA

    Google Scholar 

  35. Koza JR, Keane MA, Streeter MJ, Mydlowec W, Yu J, and Lanza G (2003) Genetic programming IV: Routine human-competitive machine intelligence, Kluwer, Dordecht

    MATH  Google Scholar 

  36. Lee MA and Takagi H (1993) Dynamic control of genetic algorithms using fuzzy logic techniques, In Forrest S (Ed.), Proceedings of the 5th International Conference on Genetic Algorithms, Morgan Kaufmmann, San Mateo, pp 76-83

    Google Scholar 

  37. Li F, Morgan R, and Williams D (1997) Hybrid genetic approaches to ramping rate constrained dynamic economic dispatch, Electric Power Systems Research, 43(11), pp. 97-103

    Article  Google Scholar 

  38. Liaw CF (2000) A hybrid genetic algorithm for the open shop scheduling problem, European Journal of Operational Research 124(1), pp. 28-42

    Article  MATH  MathSciNet  Google Scholar 

  39. Lin YC, Hwang KS, and Wang FS (2004) A mixed-coding scheme of evolutionary algorithms to solve mixed-integer nonlinear programming problems, Computers and Mathematics with Applications, 47(8-9), pp. 1295-1307

    Article  MATH  MathSciNet  Google Scholar 

  40. Lo CC and Chang WH (2000) A multiobjective hybrid genetic algorithm for the ca-pacitated multipoint network design problem, IEEE Transactions on Systems, Man and Cybernetics - Part B, 30(3), pp. 461-470

    Google Scholar 

  41. Louis SJ (1997) Working from blueprints: evolutionary learning for design, Artificial Intelligence in Engineering, 11, pp. 335-341

    Article  Google Scholar 

  42. Liu H, Abraham A, and Zhang W (2007) A Fuzzy adaptive turbulent particle swarm optimization, International Journal of Innovative Computing and Applications, 1(1), pp. 39-47

    Article  Google Scholar 

  43. Maa CY and Shanblatt MA (1992) A two-phase optimization neural network, IEEE Transaction on Neural Networks, 3(6), pp. 1003-1009

    Article  Google Scholar 

  44. Mackworth AK (1977) Consistency in networks of relations, Artificial Intelligence, 8, pp. 98-118

    Article  Google Scholar 

  45. 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), pp. 135-146

    Article  Google Scholar 

  46. Menon PP, Bates DG, and Postlethwaite I (2005) Hybrid evolutionary optimisation meth-ods for the clearance of nonlinear flight control laws, In Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, Seville, Spain, pp. 4053-4058

    Chapter  Google Scholar 

  47. Minton S, Johnston MD, Philips AB, and Laird P (1992) Minimizing conflicts: A heuristic repair method for constraint satisfaction and scheduling problems, Artificial Intelligence, 58, pp. 161-205

    Article  MATH  MathSciNet  Google Scholar 

  48. Misevicius A (2003) Genetic algorithm hybridized with ruin and recreate procedure: Application to the quadratic assignment problem, Knowledge-Based Systems, 16, pp. 261-268

    Article  Google Scholar 

  49. Morris P (1993) The breakout method for escaping from local minima, In Proceedings on 11th National Conference on Artificial Intelligence, Washington DC, pp. 40-45

    Google Scholar 

  50. Moscato P (1999) Memetic algorithms: A short introduction, In New Ideas in Optimisa-tion, Corne et al. D (Eds.), pp. 219-234

    Google Scholar 

  51. Myung H and Kim JH (1996) Hybrid evolutionary programming for heavily constrained problems, Biosystems, 38(1), pp. 29-43

    Article  Google Scholar 

  52. Neppalli VR, Chen CL, and Gupta JND (1996) Genetic algorithms for the two stage bicriteria flowshop problem, European Journal of Operational Research, 95, pp. 356-373

    Article  MATH  Google Scholar 

  53. Von Neumann J (1966) In Burks A (Ed.), Theory of self reproducing automata, University of Illinois Press, Champaign, IL

    Google Scholar 

  54. Oman S and Cunningham P (2001) Using case retrieval to seedgenetic algorithms, Inter-national Journal of Computational Intelligence and Applications, 1(1), pp. 71-82

    Article  Google Scholar 

  55. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control, IEEE Control Systems Magazine, 22(3), pp. 52-67

    Article  MathSciNet  Google Scholar 

  56. Rechenberg I (1973) Evolutionsstrategie: Optimierung technischer Systeme nach Prinzip-ien der biologischen Evolution, Stuttgart, Fromman-Holzboog

    Google Scholar 

  57. Schlottmann F and Seese D (2004) A hybrid heuristic approach to discrete multi-objective optimization of credit portfolios, Computational Statistics and Data Analysis, 47(2), pp. 373-399

    Article  MATH  MathSciNet  Google Scholar 

  58. Schwefel HP (1977) Numerische Optimierung von Computermodellen mittels der Evolutionsstrategie, Basel, Birkhaeuser

    Google Scholar 

  59. Shi XH, Liang YC, Lee HP, Lu C, and Wang LM (2005) An improved GA and a novel PSO-GA-based hybrid algorithm, Information Processing Letters, 93(5), pp. 255-261

    Article  MATH  MathSciNet  Google Scholar 

  60. Sinha A and Goldberg DE (2003) A Survey of hybrid genetic and evolutionary algorithms, ILLIGAL Technical Report 2003004

    Google Scholar 

  61. Somasundaram P, Lakshmiramanan R, and Kuppusamy K (2005) Hybrid algorithm based on EP and LP for security constrained economic dispatch problem, Electric Power Sys-tems Research, 76(1-3), pp. 77-85

    Article  Google Scholar 

  62. Storn R and Price K (1997) Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces, Journal of Global Optimization, 11(4), pp. 341-359

    Article  MATH  MathSciNet  Google Scholar 

  63. Swain AK and Morris AS (2000) A novel hybrid evolutionary programming method for function optimization, In Proceedings of the Congress on Evolutionary Computation (CEC2000), pp. 1369-1376

    Google Scholar 

  64. Tan KC, Yu Q, Heng CM, and Lee TH (2003) Evolutionary computing for knowledge discovery in medical diagnosis, Artificial Intelligence in Medicine, 27(2), pp. 129-154

    Article  Google Scholar 

  65. Tseng LY and Liang SC (2005) A hybrid metaheuristic for the quadratic assignment problem, Computational Optimization and Applications, 34(1), pp. 85-113

    MathSciNet  Google Scholar 

  66. Vargas LS, Quintana VH, and Vannelli A (1993) A tutorial description of an interior point method and its application to security-constrained economic dispatch, IEEE Transaction on Power Systems, 8(3), pp. 1315-1324

    Article  Google Scholar 

  67. Vazquez M and Whitley D (2000) A hybrid genetic algorithm for the quadratic assign-ment problem, In Proceedings of Genetic and Evolutionary Computation Conference (GECCO), Morgan Kaufmann, San Mateo, CA, pp. 135-142

    Google Scholar 

  68. Zhong YW and Yang JG (2004) A hybrid genetic algorithm with Lamarckian individ-ual learning for tasks scheduling, IEEE International Conference on Systems, Man and Cybernetics, Hague, Netherlands, pp. 3343-3348

    Google Scholar 

  69. Zmuda MA, Rizki MM, and Tamburino LA (2003) Hybrid evolutionary learning for synthesizing multi-class pattern recognition systems, Applied Soft Computing, 2(4), pp. 269-282

    Article  Google Scholar 

  70. Yang M, Zhang X, Li X, and Wu X (2002) A hybrid genetic algorithm for the fitting of models to electrochemical impedence data, Journal of Electroanalytical Chemistry, 519, pp. 1-8

    Article  Google Scholar 

  71. Wang L (2005) A hybrid genetic algorithm-neural network strategy for simulation opti-mization, Applied Mathematics and Computation, 170(2), pp. 1329-1343

    Article  MATH  MathSciNet  Google Scholar 

  72. Wang L, Zhang L, and Zheng DZ (2006) An effective hybrid genetic algorithm for flow shop scheduling with limited buffers, Computers and Operations Research, 33(10), pp. 2960-2971

    Article  MATH  MathSciNet  Google Scholar 

  73. Wolpert DH and Macready WG (1997) No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, 1(1), pp. 67-82

    Article  Google Scholar 

  74. http://www.sciencedirect.com

  75. http://www.springerlink.com

  76. http://ieeexplore.ieee.org

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science Faculty of Mathematics and Computer Science, Babes-Bolyai University, Kogalniceanu 1, 400084, Cluj-Napoca, Romania

    Dr. Crina Grosan

  2. Center of Excellence for Quantifiable Quality of Service (Q2S), Norwegian University of Science and Technology, O.S. Bragstads plass 2E, N-7491, Trondheim, Norway

    Ajith Abraham

Authors
  1. Dr. Crina Grosan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ajith Abraham
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Norwegian University of Science and Technology, O.S. Bragstads plass 2E, N-7491, Trondheim, Norway

    Ajith Abraham

  2. Babes-Bolyai University, Kogalniceanu 1, 400084, Cluj-Napoca, Romania

    Crina Grosan

  3. Osaka Prefecture University, 1-1 Gakuen-cho, 599-8531, Naka-ku, Sakai Osaka, Japan

    Hisao Ishibuchi

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Grosan, C., Abraham, A. (2007). Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews. In: Abraham, A., Grosan, C., Ishibuchi, H. (eds) Hybrid Evolutionary Algorithms. Studies in Computational Intelligence, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73297-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73297-6_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73296-9

  • Online ISBN: 978-3-540-73297-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation