A hybrid genetic algorithm with boltzmann convergence properties

Journal of Optimization Theory and Applications

Volumn 136, Issue 3, 2008, Pages 431-443

  Jackson, W C ^a   Norgard, J D ^b  

^a SpaceDev Inc ^*  (United States)

^b University of Colorado Colorado Springs   (United States)

Author keywords

Combinatorial optimization; Genetic algorithms; Hybrid algorithms; Simulated annealing

Indexed keywords

GENETIC ALGORITHMS; SIMULATED ANNEALING; STOCHASTIC SYSTEMS; TRAVELING SALESMAN PROBLEM;

ANNEALING ALGORITHM; BOLTZMANN; COMBINATORIAL OPTIMIZATION PROBLEMS; CONVERGENCE PROPERTIES; GLOBAL SEARCH ALGORITHM; HYBRID ALGORITHMS; HYBRID GENETIC ALGORITHMS; PRIORI KNOWLEDGE; SOLUTION SPACE; STOCHASTICS;

COMBINATORIAL OPTIMIZATION;

EID: 39749098717     PISSN: 00223239     EISSN: 15732878     Source Type: Journal    
DOI: 10.1007/s10957-007-9308-8     Document Type: Article

References (14)

1. Goldberg, D.: A note on Boltzmann tournament selection for genetic algorithms and population-oriented simulated annealing. Complex Syst. 4, 445-460 (1990)
2. De La Maza, M., Tidor, B.: Increased flexibility in genetic algorithms: the use of variable Boltzmann selective pressure to control propagation. Comput. Sci. Oper. Res., 425-440 (1992)
3. Adler, D.: Genetic algorithms and simulated annealing: a marriage proposal. Proc. IEEE Int. Conf. Neural Netw. 24, 1104-1109 (1993)
4. Lin, F., Kao, C., Hsu, C.: Applying the genetic approach to simulated annealing in solving some NP-hard problems. IEEE Trans. Syst., Man Cybern. 23(6), 1752-1767 (1993)
5. Kido, T., Takagi, K., Nakanishi, M.: Analysis and comparisons of genetic algorithm, simulated annealing, TABU search, and combination algorithm. Informatica 18(4), 399-410 (1994)
6. Yip, P., Pao, Y.: Combinatorial optimization with use of guided evolutionary simulated annealing. IEEE Trans. Neural Netw. 6(2), 290-295 (1995)
7. Cho, H., Oh, S., Choi, D.: A new evolutionary programming approach based on simulated annealing with local cooling schedule. Proc. IEEE Int. Conf. Evol. Comput., 598-602 (1998)
8. Grohall, G., Jung, J.: Multiple objective step function maximization with genetic algorithms and simulated annealing, October 2003. Available at SSRN: http://ssrn.com/abstract=828666
9. Hiroyasu, T., et al.: Energy minimization of protein tertiary structure by parallel simulated annealing using genetic crossover. Genetic and Evolutionary Computation Conference, pp. 49-52. GECCO-2002 Workshop Program, New York (2002)
10. Pakhira, M.K.: A hybrid genetic algorithm using probabilistic selection. J. Inst. Eng., India 84, 23-30 (2003)
11. Varanelli, J.M., Cohoon, J., Martin, W.N.: Population-oriented simulated annealing: an evolutionary-thermodynamic hybrid approach to VLSI network partitioning. In: Handbook of Evolutionary Computation. IOP Publishing Ltd. and Oxford University Press (1997). Sect. G3.5
12. Jackson, W.C., Norgard, J.: A hybrid genetic algorithm for generating optimal synthetic aperture radar target servicing strategies. Proc. IEEE Aerosp. Conf. (2001)
13. Jackson, W.C., Carpenter, B.: Optimal placement of spacecraft sun sensors using stochastic optimization. Proc. IEEE Aerosp. Conf. (2004)
14. Jackson, W.C.: Minimum energy maneuvering strategies for multiple satellite inspection missions. Proc. IEEE Aerosp. Conf. (2005)