Jonathan L. Shapiro and Adam Prugel-Bennett. Maximum entropy analysis of genetic algorithm operators. Lecture Notes in Computer Science, 993:14-24, 1995.  Home/Search Document Details and Download
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Random Heuristic Search - Vose (1999) (2 citations) (Correct)
.... sets (theorem 12) and so the average of [t] is a natural candidate to represent [t] One might alternatively pick a maximal element of [t] with respect to entropy, for instance, as a representative (models employing some sort of maximum entropy assumption are not uncommon; see, for example, [11,15,17,18]) These two possibilities coincide, however. An element x 2 n is said to be dominated by j, denoted x OE j, provided i j j = x i = x j Theorem 16 If Xix = Xiy and x OE j, then the entropy of x is greater than or equal to that of y. Theorem 17 Let T be the set of equivalence class ....
J. Shapiro and A. Prugel-Bennett, Maximum Entropy Analysis of Genetic Algorithm Operators, in: Lecture Notes in Computer Science, 993 (SpringerVerlag, Berlin, 1995) 14--24.
Models and Search Strategies for Applied Molecular Evolution - Levitan (1997) (Correct)
.... melt all over the landscape [28, 29, 36, 155] Thus, rather than constantly increasing stringency, searches with mutation may be better served with intermediate stringencies, at least until the last few rounds of the search. With crossover, there is the additional concern of population diversity [19, 46, 59, 107, 114, 116, 130]. One role of crossover is to add to the library sequences very distant (Hamming distance) from those already in the population. For this to happen, the library must consist of sufficiently diverse species so the recombined fragments differ. With just a few species in the population (low ....
....a given threshold in terms of all parameters in the model. These newer GA studies also suggest approaches for deriving selection stringency and mutation rate schedules. Shapiro and Prugel Bennett demonstrate a mutation schedule that maximizes the average fitness of the best member in a population [130]. Back describes a means to set a mutation rate schedule based on calculating the optimal mutation distance given the population s average fitness [5] The derivation is for a single, simple landscape, but is generalizable in principle. There are also several procedures suggested for setting ....
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J. L. Shapiro and A. Prugel-Bennett. Maximum entropy analysis of genetic algorithm operators. Lecture Notes in Computer Science, 993:14--24, 1995.
Scaling of Probability-Based Optimization Algorithms - Shapiro Department Of Self-citation (Shapiro) (Correct)
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Jonathan L. Shapiro and Adam Prugel-Bennett. Maximum entropy analysis of genetic algorithm operators. Lecture Notes in Computer Science, 993:14-24, 1995.
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