Browse > Article

An Experimental Comparison of Adaptive Genetic Algorithms  

Yun, Young-Su (조선대학교 경상대학 경영학부)
Publication Information
Journal of the Korean Operations Research and Management Science Society / v.32, no.4, 2007 , pp. 1-18 More about this Journal
Abstract
In this paper, we develop an adaptive genetic algorithm (aGA). The aGA has an adaptive scheme which can automatically determine the use of local search technique and adaptively regulate the rates of crossover and mutation operations during its search process. For the adaptive scheme, the ratio of degree of dispersion resulting from the various fitness values of the populations at continuous two generations is considered. For the local search technique, an improved iterative hill climbing method is used and incorporated into genetic algorithm (GA) loop. In order to demonstrate the efficiency of the aGA, i) a canonical GA without any adaptive scheme and ii) several conventional aGAs with various adaptive schemes are also presented. These algorithms, including the aGA, are tested and analyzed each other using various test problems. Numerical results by various measures of performance show that the proposed aGA outperforms the conventional algorithms.
Keywords
Adaptive Genetic Algorithm; Adaptive Scheme; Local Search Technique;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Eiben, A.E., R. Hinterding, and Z. Michalewicz, Parameter control in evolutionary algorithms, IEEE Transactions on Evolution Computation, Vol.3, No.2(1999), pp.124-141   DOI   ScienceOn
2 Fogel, D.B. G.B. Fogel, and K. Ohkura, Multiple-vector self-adaptation in evolutionary algorithms, BioSystems, No.61(2001), pp.155-162   DOI
3 Rabi, V. and B.S.N. Murty, P.J. Reddy, Nonequilibrium simulated annealing algorithm applied to reliability optimization of complex systems, IEEE Transactions on Reliability, Vol.46, No.2(1997), pp.233-239   DOI   ScienceOn
4 Sandgren, E., Nonlinear integer and discrete programming in mechanical design optimization, ASME Journal of Mechanical Design, Vol.112, No.2(1990), pp.223-229   DOI
5 Yun, Y.S., Genetic algorithm with fuzzy logic controller for preemptive and non-preemptive job shop scheduling problems, Computers and Industrial Engineering, Vol.43, No.3(2002), pp.623-644   DOI   ScienceOn
6 Wu, S.J., and P.T. Chow, Genetic algorithms for nonlinear mixed discrete-integer optimization problems via meta-genetic parameter optimization, Engineering Optimization, No.24(1995), pp.137-159
7 Shuguang, Z. and J. Licheng, Multi-objective evolutionary design and knowledge discovery of logic circuits based on an adaptive genetic algorithm, Genetic Programming and Evolvable Machines, No.7(2006), pp.195-210
8 Gen, M. and R. Cheng, Genetic Algorithms and Engineering Design, John Wiley and Son, 1997
9 Davis, L., Handbook of Genetic Algorithms, Van Nostrand Reinhold, 1991
10 Hong, T.P., and H.S. Wang, A dynamic mutation genetic algorithm, Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, No.3(1996), pp.2000- 2005
11 Michalewicz, Z., Genetic Algorithms + Data Structures = Evolution Program, Second Extended Edition, Spring-Verlag, 1994
12 Hong, T.P., H.S. Wang, W.Y. Lin, and W.Y. Lee, Evolution of appropriate crossover and mutation operators in a genetic process, Applied Intelligence, No.16(2002), pp.7-17
13 Lee, C.Y., Y.S. Yun, and M. Gen, Reliability optimization design for complex systems by hybrid GA with fuzzy logic control and local search. IEICE Transaction on Fundamentals, E85-A(4) : (2002), pp.880-891
14 Mak, K.L., Y.S. Wong, and W.W. Wang, An adaptive genetic algorithm for manufacturing cell formation, International Journal of Manufacturing Technology, No.16(2000), pp. 491-497
15 Herrera, F., and M. Lozano, Fuzzy adaptive genetic algorithms : design, taxonomy and future directions, Soft Computing, Vol.7, No.8(2003), pp.545-562   DOI   ScienceOn
16 Srinvas, M. and L.M. Patnaik, Adaptive Probabilities of crossover and mutation in genetic algorithms, IEEE Transaction on Systems, Man and Cybernetics, Vol.24, No.4 (1994), pp.656-667   DOI   ScienceOn
17 Angeline, P.J., Adaptive and self-adaptive evolutionary computations, in : M. Palaniswami, Y. Attikiouzel, R. Markc, D. Fogel, T. Fukuda, (Eds), Computational Intelligence: A Dynamic Systems Perspective, Piscataway, NJ : IEEE Press, 1995, pp.152-163
18 De Jong, K.A., Analysis of the behavior of a class of genetic adaptive systems, PhD Thesis, University of Michigan (University Microfilms 1975, pp.76-9381
19 Hoffmeister, F., and T. Back, Genetic algorithms and evolution strategies : similarities and differences, Proceedings of the 1st Workshop on Parallel Problem Solving from Nature (PPSN1), 1991, pp.455-471
20 Amir, H.M. and T. Hasegawa, Nonlinear mixed-discrete structural optimization, Journal of Structural Engineering, Vol.115, No.3(1989), pp.626-646   DOI   ScienceOn
21 Yen, J., J.C. Liao, B.J. Lee, and D. Randolph, A hybrid approach to modeling metabolic systems using a genetic algorithm and simplex method. IEEE Transactions on Systems, Man, and Cybernetics-Part B : Cybernetics, Vol.28, No.2(1998), pp.173-191   DOI   ScienceOn
22 Grefenstette, J.J., Optimization of control parameters for genetic algorithms, IEEE Transactions on Systems, Man, and Cybernetics, No.16(1986), pp.122-128
23 Wang, P.T., G.S. Wang, and Z.G. Hu, Speeding up the search process of genetic algorithm by fuzzy logic, Proceedings of the 5th European Congress on Intelligent Techniques and Soft Computing, 1997, pp.665- 671
24 Li, B. and W. Jiang, A novel stochastic optimization algorithm. IEEE Transactions on Systems, Man, and Cybernetics-Part B : Cybernetics, Vol.30, No.1(2000), pp.193-198   DOI   ScienceOn
25 Yun, Y.S. and C.U. Moon, Comparison of adaptive genetic algorithms for engineering optimization problems, International Journal of Industrial Engineering, Vol.10, No.4(2003), pp.584-590
26 Wu, Q.H., Y.J. Cao, and J.Y. Wen, Optimal reactive power dispatch using an adaptive genetic algorithm, Electrical Power and Energy Systems, Vol.20, No.8(1998), pp.563- 569   DOI   ScienceOn
27 Espinoza, F.P., B.S. Minsker, and D.E. Goldberg, A self adaptive hybrid genetic algorithm, Proceedings on the Genetic and Evolutionary Computation Conference, San Francisco, Morgan Kaufman Publishers, 2001