A. H. Wright and Y. Zhao. Markov chain models of genetic algorithms. In Proceedings of the Genetic and Evolutionary Computation (GECCO) conference, pages 734--742, San Francisco, CA., 1999. Morgan Kaufmann Publishers. 15  Home/Search Document Details and Download
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Towards a Rational Methodology for Using Evolutionary Search.. - Sharpe (2000) (Correct)
....1991) Initially this approach looked like an impressive avenue of research. A number of convergence results have been proved and other esoteric characteristics of the simple GA have been derived within this framework (Koehler, 1997; Suzuki, 1997; Spears De Jong, 1997; Cantu Paz, 1998; Wright Zhao, 1999). Unfortunately the results from the application of such rigorous analytic techniques have some quite serious shortcomings when it comes to drawing useful conclusions about how to use GAs on real problems. To make the mathematics tractable it is usually necessary to deal only with the smallest or ....
....5.1. 1 Markov chain models A fairly successful area of ESA theoretical research has been the application of Markov chains to simple ESAs to obtain results about the expected long term behaviour of the ESA population (Nix Vose, 1991; Spears De Jong, 1997; Suzuki, 1997; Cantu Paz, 1998; Wright Zhao, 1999). These results are, in effect, results about the accessibility of certain regions of the search space. Considering the formidable complexity of the dynamical systems created when an ESA is run on a search space, it is impressive that any such analytic convergence results have been achieved at ....
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Wright, A., & Zhao, Y. (1999). Markov chain models of genetic algorithms. In GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference.
Analysis of Recombinative Algorithms on a Non-Separable.. - Watson (2000) (7 citations) (Correct)
....the existence of a path, and the time for a step, are dependent on the state of the population when using recombination. However, to calculate an upper bound on the expected time it is sufficient to know certain properties, or invariants, of the population rather than its exact state. For example, Wright and Zhao (1999) provide an analysis of a recombinative algorithm on a separable building block problem by using the property of the algorithm that prevents alleles from being lost. Under these conditions (that we will detail shortly) there is always some recombination operation that will improve the fitness of ....
....hyperplanes. In H IFF the operation of recombination may be defeated if the population is allowed to converge at even one locus. Secondly, most analyses assume separable problems, and, surprisingly often, focus on the extreme case where every bit is separable the max ones problem. However, Wright and Zhao (1999) provide an approach to analysis that, although directed at separable building block problems, can be adapted for our purposes. Their approach is to prove that there is always a way to improve fitness, and then to give a solution time based on the product of the length of the path to the solution, ....
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Wright, AH, & Zhao, Y, 1999, "Markov Chain Models of Genetic Algorithms", Procs. of GECCO'99, Banzhaf, et al. eds., Morgan Kauffman, pp. 734-741.
Continuous Dynamical System Models of Steady-State Genetic .. - Alden Wright Computer Self-citation (Wright) (Correct)
....20 runs. The error bars represent one standard deviation. worst element and random deletion. Further work is needed to investigate the properties of fixed points for these and other deletion methods. The relationship of these models to the Markov chain models of steady state algorithms given in [WZ99] could also be investigated. Acknowledgments The first author thanks Alex Agapie for discussions regarding section 3. ....
A. H. Wright and Y. Zhao. Markov chain models of genetic algorithms. In Proceedings of the Genetic and Evolutionary Computation (GECCO) conference, pages 734--742, San Francisco, CA., 1999. Morgan Kaufmann Publishers. 15
Real Royal Road Functions - Where Crossover . . . - Wegener (2001) (Correct)
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Wright, A. H., and Zhao, Y. (1999). Markov chain models of genetic algorithms. Proc. of the Genetic and Evolutionary Computation Conference (GECCO), 734-741, Morgan Kaufmann, SanMateo, Calif. 14
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