DEAL: A Direction-Guided Evolutionary Algorithm

  • Cuong C. Vu
  • Lam Thu Bui
  • Hussein A. Abbass
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7673)


In this paper, we propose a real-valued evolutionary algorithm being guided by directional information. We derive direction of improvement from a set of elite solutions, which is always maintained overtime. A population of solutions is evolved over time under the guidance of those directions. At each iteration, there are two types of directions that are being generated: (1) convergence direction between an elite solution (stored in an external set) and a second-ranked solution from the current population, and (2) spreading direction between two elite solutions in the external set. These directions are then used to perturb the current population to get an offspring population. The combination of the offsprings and the elite solutions is used to generate a new set of elite solutions as well as a new population. A case study has been carried out on a set of difficult problems investigating the performance and behaviour of our newly proposed algorithm. We also validated its performance with 12 other well-known algorithms in the field. The proposed algorithm showed a good performance in comparison with these algorithms.


direction of improvement evolutionary algorithms 


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  1. 1.
    Back, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York (1996)Google Scholar
  2. 2.
    Corne, D., Dorigo, M., Glover, F.: New Ideas in Optimization. McGraw Hill, Cambridge (1999)Google Scholar
  3. 3.
    Dasgupta, D.: Artificial Immune Systems and Their Applications. Springer, Berlin (1998)Google Scholar
  4. 4.
    Deb, K.: Multiobjective Optimization using Evolutionary Algorithms. John Wiley and Son Ltd., New York (2001)Google Scholar
  5. 5.
    Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Systems 9, 115–148 (1995)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Deb, K., Anand, A., Joshi, D.: A computationally efficient evolutionary algorithm for real-parameter optimization. Evolutionary Computation 4, 371–395 (2002)CrossRefGoogle Scholar
  7. 7.
    Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, USA (2004)CrossRefzbMATHGoogle Scholar
  8. 8.
    Fogel, L.J., Angeline, P.J., Fogel, D.B.: An evolutionary programming approach to self-adaptation in finite state machines. In: McDonnell, J.R., Reynolds, R.G., Fogel, D.B. (eds.) Proc. of Fourth Annual Conference on Evolutionary Programming, pp. 355–365. MIT Press, Cambridge (1995)Google Scholar
  9. 9.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)zbMATHGoogle Scholar
  10. 10.
    Goldberg, D.E.: The design of innovation: lessons from and for competent genetic algorithms. Kluwer Academic Publishers, Massachusetts (2002)zbMATHGoogle Scholar
  11. 11.
    Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9, 159–195 (2001)CrossRefGoogle Scholar
  12. 12.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  13. 13.
    Albert, Y.S., Lam, V.O.K.: Li, and James J.Q. Yu. Real-coded chemical reaction optimization. IEEE Transactions on Evolutionary Computation (accepted for publication, 2012)Google Scholar
  14. 14.
    Larraanaga, P., Lozano, J.A.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Norwell (2002)Google Scholar
  15. 15.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, London (1996)zbMATHGoogle Scholar
  16. 16.
    Mitchell, T.: Machine Learning. McGraw Hill, Singapore (1997)zbMATHGoogle Scholar
  17. 17.
    Price, K., Storn, R., Lampinen, J.: Differential Evolution - A Practical Approach to Global Optimization. Springer, Berlin (2005)zbMATHGoogle Scholar
  18. 18.
    Rudolph, G.: Evolution strategy. In: Handbook of Evolutionary Computation. Oxford University Press (1997)Google Scholar
  19. 19.
    Storn, R., Price, K.: Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report tr-95-012. Technical report, ICSI (1995)Google Scholar
  20. 20.
    Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3(2), 82–102 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Cuong C. Vu
    • 1
  • Lam Thu Bui
    • 1
  • Hussein A. Abbass
    • 2
  1. 1.Le Quy Don Technical UniversityVietnam
  2. 2.University of New South WalesAustralia

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