On a New Evolutionary Algorithm for Network Optimization Problems

네트워크 문제를 위한 새로운 진화 알고리즘에 대하여

  • Published : 2007.06.30

Abstract

This paper focuses on algorithms based on the evolution, which is applied to various optimization problems. Especially, among these algorithms based on the evolution, we investigate the simple genetic algorithm based on Darwin's evolution, the Lamarckian algorithm based on Lamark's evolution and the Baldwin algorithm based on the Baldwin effect and also Investigate the difference among them in the biological and engineering aspects. Finally, through this comparison, we suggest a new algorithm to find more various solutions changing the genotype or phenotype search space and show the performance of the proposed method. Conclusively, the proposed method showed superior performance to the previous method which was applied to the constrained minimum spanning tree problem and known as the best algorithm.

Keywords

References

  1. Assad, A. and W. Xu, 'The Quadratic Minimum Spanning Tree Problem,' Naval Research Logistics, Vol.39(1992), pp.399-417 https://doi.org/10.1002/1520-6750(199204)39:3<399::AID-NAV3220390309>3.0.CO;2-0
  2. Eckert, C. and J. Gottlieb, 'Direct Representation and Variation Operators for the Fixed Charge Transportation Problem,' Lecture Notes in Computer Science, Vol.2439(2002), pp.77-87 https://doi.org/10.1007/3-540-45712-7_8
  3. Gen, M. and R. Chen, Genetic Algorithms and Engineering Design, Wiley, 1997
  4. Goldberg, D, Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, MA, 1989
  5. Grefenstette, J.J., 'Lamarckian Learning in Multi-agent Environments,' In Proeedings of 4th International Conference of Genetic Algorithms, (1991), pp.303-310
  6. Gruau, F. and D. Whitley, 'Adding learning to the cellular development of neural networks: evolution and the Baldwin effect,' Evolutionary Computation, Vol.1, No.3(1993), pp.213-233 https://doi.org/10.1162/evco.1993.1.3.213
  7. Hinton, G.E and S.J. Nowlan, 'How Learning Can Guide Evolution,' Complex Systems, Vol.1(1997), pp.495-502
  8. Holland, J.H., Adaption in Natural and Artificial Systems, MIT Press, 1992
  9. Houck, C.R., J.A. Joines, and M.G. Kay, 'Utilizing lamarckian evolution and the Baldwin effect in hybrid genetic algorithms,' Meta-Heuristic Res. Appl. Group, Dept. Ind. Eng., North Carolina State Univ., NCSU-IE Tech Rep. 96-1, 1996
  10. Julstroml, BA, 'Comparing darwinian, baldwinian, and lamarckian search in a genetic algorithm for the -l-cycle problem,' Late Breaking Paper at the 1997 Genetic and Evolutionary Computation Conference, 1999
  11. Mayley, G., 'Landscapes, Learning Costs and Genetic Assimilation,' Evolutionary Computation, Vol.4, No.3(1996), pp.213-234 https://doi.org/10.1162/evco.1996.4.3.213
  12. Merz, P and B. Freisleben, 'Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning,' Evolutionary Computation, Vol.8, No.1(2000), pp.61-91 https://doi.org/10.1162/106365600568103
  13. Moscato, P., 'On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts Toward memetic Algorithms,' Tech Rep. No. 790, California Institute of Technology, 1989
  14. Raidl, G.R. and J. Gottlieb, 'Empirical Analysis of Locality, Heritability and Heuristic Bias in Evolutionary Algorithms : A Case Study for the Multidimensional Knapsack Problem,' Evolutionary Computation, Vol.13, No.4(2005), pp.441-475 https://doi.org/10.1162/106365605774666886
  15. Ross, B.J., 'A Lamarckian Evolution Strategy for Genetic Algorithms,' in Practical Handbook of Genetic Algorithms: Complex Coding Systems, 1999
  16. Turney, P., 'Myths and legends of the Baldwin Effect,' In proceedings of the Workshop on Evolutionary Computing and Machine Learning at the 13th International Conference on Machine Learning, (1996), pp.135-142
  17. Sendhoff, B., M. Kreutz, and W.V. Seelen, 'A condition for the genotype-phenotype mapping: Casualty,' Proceedings of the Seventh International Conference on Genetic Algorithms, Morgan Kauffman, (1997), pp.73-80
  18. Soak, S.M., H.G. Lee, and S.C. Byun, 'Subtour Preservation Crossover Operator for the Symmetric TSP,' Journal of the Korea Institute of Industrial Engineers, 33(2), accepted, 2006
  19. Whitley, D., 'A genetic algorithm tutorial,' Statics and Computing, Vol.4(1994), pp.65-85
  20. Whitley, D., V.S. Gordon, and K.E. Mathias, 'Lamarckian Evolution, The Baldwin Effect and Function Optimization,' Parallel Problem Solving from Nature III, (1994), pp.6-15
  21. Xu, W., 'On the quadratic minimum spanning tree problem,' Proc. of the Third Int. Conf. on Genetic algorithms, ed. J. Schaffer. Morgan Kaufmann Publishers, San Mateo, California, (1995), pp.141-148
  22. Zhou, G. and M. Gen, 'An Effective GA Approach to The Quadratic Minimum Spanning Tree Problem,' Computers & Operations Research, Vol.25, No.3(1998), pp.229-237 https://doi.org/10.1016/S0305-0548(97)00039-7