Advances in Neural Information Processing Systems, 5, 123-130. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....Considered Harmful John J. Grefenstette Navy Center for Applied Research in Artificial Intelligence Code 5514 Naval Research Laboratory Washington, DC 20375 5000 E mail: GREF AIC.NRL.NAVY.MIL Abstract A central problem in the theory of genetic algorithms is the characterization of problems that are difficult for GAs to optimize. Many attempts to characterize such problems focus on the notion of Deception, defined in terms of the static average ....

....the study of Deception, consider the following problem: max f (x 1 , x 2 ) where 0 x i 1, and f (x 1 , x 2 ) # # # 2(1 x 1 ) 2 10x 2 2 x 1 2 10x 2 2 if x 2 0.995 if x 2 0. 995 Assume that x 1 and x 2 are represented in binary notation on a chromosome of length 20, using 10 bits for each argument. The optimal solution is (x 1 , x 2 ) 0, 1) Using the terminology in (Whitley, 1991) this problem has a fully deceptive subproblem of order 10 , meaning that in every schema competition defined over the first 10 bit positions, the expected winner of each such ....

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In L. Davis (Ed.), Genetic algorithms and simulated annealing (pp. 74-88). London: Pitman. Goldberg, D. E. (1989a). Genetic algorithms in search, optimization, and machine learning. Reading: Addison-Wesley.

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Advances in Neural Information Processing Systems, 5, 123-130. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning.

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