An Ant System Extrapolated Genetic Algorithm

개미 알고리즘을 융합한 적응형 유전알고리즘

  • Published : 2005.08.01

Abstract

This paper Proposes a novel adaptive genetic algorithm (GA) extrapolated by an ant colony optimization. We first prove that the algorithm converges to the unique global optimal solution with probability arbitrarily close to one and then, by experimental studies, show that the algorithm converges faster to the optimal solution than GA with elitism and the population average fitness value also converges to the optimal fitness value. We further discuss controlling the tradeoff of exploration and exploitation by a parameter associated with the proposed algorithm.

본 논문에서는 개미 군 집단 알고리즘을 융합한 새로운 적응형 유전 알고리즘을 제안하고, 제안된 알고리즘이 확률적으로 최적 해에 수렴함을 증명한다. 실험을 통해서, 제안된 알고리즘은 최적 해로의 수렴이 어려운 여러 가지 대표적인 함수들에 대하여 elitist 전략을 사용한 유전 알고리즘보다 더 빠른 속도로 최적 해에 수렴하고 한 군집 내의 모든 해들이 최적 해로 수렴하며 파라미터 값에 따라 새로운 탐색이나 현 상태로의 귀착의 정도를 조절할 수 있는 유연성 있는 알고리즘인 것을 보인다.

Keywords

References

  1. G. Rudolph, 'Convergence Analysis of Canonical Genetic Algorithms,' IEEE Trans. on Neural Networks, vol. 5, no. 1, 96-101, 1994 https://doi.org/10.1109/72.265964
  2. R. Eberhart and J. Kennedy, Swarm Intelligence, Morgan Kaufmann, 2001
  3. C. R. Reeves, 'Genetic Algorithms for the Operations Resercher,' INFORMS J. on Computing, vol.9, no. 3, 231-250, 1997 https://doi.org/10.1287/ijoc.9.3.231
  4. H. Muhlenbein, G. Paab, 'From Recombination of Genes to the Estimation of Distributions I. Binary parameters,' Parallel Problem Solving from Nature, H. Voigt, W. Ebeling, I. Rechenberg, and H. Schwefel (eds.), LNCS, vol. 1141, 178-187, 1996 https://doi.org/10.1007/3-540-61723-X_982
  5. S. K. Shakya, 'Probabilistic Model Building Genetic Algorithm(PMBGA): A survey,' Tech. Rep., School of Computing, The Robert Gordon University, UK, 2003
  6. M. Dorigo, V. Maniezzo, A. Colorni, 'The Ant System: Optimization by a Colony of Cooperating Agents,' IEEE Trans. Systems Man Cybernet., vol. 25, 29-41, 1996 https://doi.org/10.1109/3477.484436
  7. W. J. Gutjahr, 'A Graph-based Ant System and Its Convergence,' Future Generation Computer Systems, vol. 16, no. 8, 2000
  8. M. Srinivas and L. M. Patnaik, 'Adaptive Probabilities of Crossover and Mutation in Genetic Algorithm,' IEEE Trans. on Systems, Man and Cybernetics, vol. 24, no. 4, 656-667, 1994 https://doi.org/10.1109/21.286385
  9. J. Yang, J. Horng, and C. Kao, 'A genetic algorithm with adaptive mutations and family competition for training neural networks,' Int. J. of Neural Systems, vol. 10, no. 5, 333-352, 2000 https://doi.org/10.1016/S0129-0657(00)00031-4
  10. D. Thierens, 'Adaptive mutation rate control schemes in genetic algorithms,' in Proc. of the 2002 IEEE World Congress on Computational Intelligence, 980-985, 2002 https://doi.org/10.1109/CEC.2002.1007058
  11. S. Baluja, 'Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning,' Tech. Rep. No. CMUCS94163, Department of Computer Science, Carnegie Mellon University, 1994
  12. P. Larranaga, R. Etxeberria, J.A. Lozano and J.M Pena, 'Combinational optimization by learning and simulation of Bayesian networks,' in Proc. of the Conference in Uncertainty in Artificial Intelligence, 343-352, 2000
  13. D. Bhandari, C. A. Murthy, and S. K. Pal, 'Genetic Algorithms with Elitist Model and Its Convergence,' International Journal of Pattern Recognition and Artificial Intelligence, vol. 10, 731-747, 1996 https://doi.org/10.1142/S0218001496000438
  14. K. Najim, A. S. Poznyak, and E. Ikonen, 'Optimization based on a team of automata with binary outputs,' Automatica, vol. 40, 1349-1359, 2004 https://doi.org/10.1016/j.automatica.2004.03.013
  15. K. De Jong and W. Spears, 'An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms,' in Proc. First Workshop Parallel Problem Solving from Nature, Springer-Verlag, 38-47, 1990
  16. X. Yao, Y. Liu, and G. Lin, 'Evolutionary Programming Made Faster,' IEEE Trans. on Evloutionary Computation, vol 5, no. 2, 82-102, 1999 https://doi.org/10.1109/4235.771163
  17. C. Fernandes, R. Tavares, C. Munteanu, and A. Rosa, 'Using Assortative Mating in Genetic Algorithms for Vector Quantization Problems,' in Proc. of the 2001 ACM symposium on Applied computing, 361-365, 2001 https://doi.org/10.1145/372202.3723671-58113-287-5
  18. F. Villegas, T. Cwik, Y. Rahmat-Samii, and M. Manteghi, 'A Parallel Electromagnetic Genetic-Algorithm Optimization (EGO) Application for Patch Antenna Design,' IEEE Trans. on Antennas and Propagation, vol. 52, no. 9, 2424-2435, 2004 https://doi.org/10.1109/TAP.2004.834071
  19. M. Brameier and W. Banzhaf, 'A Comparison of Linear Genetic Programming and Neural Networks in Medical Data Mining,' IEEE Trans. on Evolutionary Computation, vol. 5, 17-26, 2001 https://doi.org/10.1109/4235.910462
  20. J. Fernandez and A. Caballero, 'A Comparison of Management Strategies for Conservation with regard to Population Fitness,' Conservation Genetics 2, 121-131, Kluwer Academic Publishers, 2001 https://doi.org/10.1023/A:1011830703723
  21. M. Dorigo, G. Di Caro, and T. Stutzle, Special Issue on 'Ant Algorithms,' Future Generation Computer Systems, vol. 16, no. 8, 2000