A GA-Based Algorithm with a Very Fast Rate of Convergence (original) (raw)
References
Holland, J. H.: Genetic Algorithms and the Optimal Allocations of Trails, SIAM Journal of Computing, 2(2), (1973) 88–105. ArticleMATHMathSciNet Google Scholar
Holland, J. H.: Adaptation In Natural And Artificial Systems, Ann Arbor, MI: The University of Michigan Press, (1975) Google Scholar
Scales, E.: Introduction to Nonlinear Optimization, Springer-verlag, N. Y. (1985) Google Scholar
Bellman, R. E.: Dynamic Programming, Princetion Univ. Press, princetion, N. J., USA (1957) Google Scholar
Kirkpatrick, S., Gellat Jo, C.D., and Vecchi, M. P.: Optimization by Simulated Annealing, Science, Vol. 220, No. 4598, (1983) 671–680 ArticleMathSciNet Google Scholar
Back, T., Hoffmeister, F., and Schwefel, H. P.: A Survey of Evolution Strategies, in R. K. Belew, and L. B. Booker, Eds. Proc. 4th International Conference on Genetic Algorithms, San Mateo, CA: Morgan Kaufmann, (1991) 2–9 Google Scholar
Fogel, L. J., Owens, A. J. and Walsh, M. J.: Artificial Intelligence Through Simulated Evolution, New York: John Wiley (1966) MATH Google Scholar
Fogel, D. B.: System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling, Needham Heights, MA: Ginn Press (1991) Google Scholar
Y. T. Chan, J.M. Riley and J.B. Plant, “Modeling of Time Delay and it’s Application to Estimation of Nonstationary Delays,” IEEE Trans. Acoust. Speech, Signal Processing, Vol.: ASSP-29, pp. 577–581, June 1981. Article Google Scholar
Davidor, Y.: Genetic Algorithms & Robotics, A Heuristic Strategy for Optimization, World Scientific, 5Singapore (1991) Google Scholar
Karr, C.L.: Genetic Algorithms for Fuzzy Controllers, A.I. Expert, Vol. 6, No. 2, (1991). 26–33 ArticleMathSciNet Google Scholar
Montana, D. J. Davis, L.: Training Feed-forward Neural Networks Using Genetic Algorithms, Proc. of Int. Joint Conf. on Artificial Intelligence (Detroid), (1989) 762–767 Google Scholar
Vignaun, G. A., Michalewicz, and Z: A Genetic Algorithms For The Linear Transportation Problem,” IEEE Trans. on Systems Man and Cybernetics, Vol. 21, (1991). 445–452 Article Google Scholar
Grefenstette, J. J.: Optimization of Control Parameters for Genetic Algorithms, IEEE Trans. on Sys., Man, and Cyb., Vol. Smc-16, No. 1, (1986) Google Scholar
Odetayo, M. O.: Optimal Population Size for Genetic Algorithms: An Investigation, IEEE, Colloquium on Genetic Algorithms for Control Systems Engineering, (1993) 2/1–2/4 Google Scholar
Alander, J. T.: On Optimal Population Size of Genetic Algorithms, CompEuro’ 92. Computer Systems and Software Engineering, Proc. (1992) 65–70 Google Scholar
Arabas, J., Michalewicz, Z. and Mulawka, J.: GAVaPS — a Genetic Algorithms with Varying Population Size, Evolutionary Computation, IEEE World Congress on Computational Intelligence, Proceedings of the IEEE Conf., Vol. 1, (1994) 73–78 Article Google Scholar
Lima, J., Gracias, A. N., Pereira, H. and Rosa, A.: Fitness Function Design for Genetic Algorithms in Cost Evaluation Based Problems, Proc. of IEEE, International Conf. on Evolutionary Computation, (1996) 207–212 Google Scholar
Ghosh, A., Tsutsui, S. and Tanaka, H.: Individual Aging in Genetic Algorithms, Conf. on Intelligence Information System, Australian and New Zealand, (1996) 276–279 Google Scholar
Hesser, J., Manner, R.: Towards an Optimal Mutation Probability for Genetic Algorithms, in Proc. First Int. Workshop on Parallel Problem Solving from Nature, Dortmuntd, (1990) paper A-XII. Google Scholar
Oi, X., Palmieri, F.: Theoretical Analysis of Evolutionary Algorithms With an Infinite Population Size in Continuous Space Part I: Basic Properties of Selection and Mutation, IEEE Trans. on Neural Networks, Vol. 5, No. 1, (1994) Google Scholar
Oi, X., Palmieri, F.: Theoretical Analysis of Evolutionary Algorithms With an Infinite Population Size in Continuous Space Part II: Analysis of the Diversification Role of Crossover, IEEE Trans. on Neural Networks, vol. 5, No. 1, (1994) Google Scholar
Shang, Y. Li, G. J.: New Crossover in Genetic Algorithms, Proc. of IEEE, Third International Conf. on Tools for Artificial Intelligence, TAI’ 91, (1991) 150–153 Google Scholar
Coli, M., Gennuso, and Palazzari, G. P.: A New Crossover Operator for Genetic Algorithms, Proc. of IEEE, International Conf. on Evolutionary Computation, (1996) 201–206 Google Scholar
Potts, J. C., Giddens, T. D. and Yadav, S. B.: The Development and Evaluation of an Improved Genetic Algorithms Based on Migration and Artificial Selection, IEEE Trans. on Syst., Man, and Cyber., vol. 24, Nno. 1, (1994) Google Scholar
Moed, M. C. Stewart, C. V. and Kelly, R. B.: Reducing The Search Time of A Steady State Genetic Algorithms Using the Immigration Operator, Proc. of IEEE, Third International Conf. on Tools for Artificial Intelligence, TAI’ 91, (1991) 500–501 Google Scholar
Tsutsui, S. Fujimoto, Y.: Phenotypic Forking Genetic Algorithms (p-fGA), Proc. of IEEE, International Conf. on Evolutionary Computation, Vol. 2, (1995) 566–572 Article Google Scholar
Tsutsui, S., Fujimoto, Y. and Hayashi, I.: Extended Forking Genetic Algorithms for Order Representation (o-fGA), Proc. of the First IEEE, Conf. on IEEE World Congress on Computational Intelligence, vol. 2, (1994) 566–572 Google Scholar
Davis, L.: Handbook of Genetic Algorithms, Van Nostrand Reinhold (1991) Google Scholar
Koza J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, Cambridge, MA: MIT Press (1992) MATH Google Scholar
Miura, H, Vanderplaats, G. N., Kodiyalam S.: Experiences in Large Scale Structural Design Optimization, Applications of Supper Computer in Engineering: Fluid Flow and Stress Analysis Applications, Elsevier, Amsterdam (1989) Google Scholar