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On the Scalability of Simple Genetic Algorithms
, 1999
"... Scalable evolutionary computation has become an intensively studied research topic in recent years. The issue of scalability is predominant in any eld of algorithmic design, but it became particularly relevant for the design of competent genetic algorithms once the scalability problems of simple gen ..."
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Cited by 2 (0 self)
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Scalable evolutionary computation has become an intensively studied research topic in recent years. The issue of scalability is predominant in any eld of algorithmic design, but it became particularly relevant for the design of competent genetic algorithms once the scalability problems of simple
Scalability Problems of Simple Genetic Algorithms
 Evolutionary Computation
, 1999
"... Scalable evolutionary computation has become an intensively studied research topic in recent years. The issue of scalability is predominant in any field of algorithmic design, but it became particularly relevant for the design of competent genetic algorithms once the scalability problems of simpl ..."
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Cited by 49 (5 self)
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of simple genetic algorithms were understood. Here we present some of the work that has aided in getting a clear insight in the scalability problems of simple genetic algorithms. Particularly, we discuss the important issue of building block mixing. We show how the need for mixing places a boundary
Simple Genetic Algorithms with Linear Fitness
 Evolutionary Computation
, 1994
"... A general form of stochastic search is described (random heuristic search) and some of its general properties are proved. This provides a framework in which the simple genetic algorithm (SGA) is a special case. The framework is used to illuminate relationships between seemingly different probabilist ..."
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Cited by 32 (6 self)
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A general form of stochastic search is described (random heuristic search) and some of its general properties are proved. This provides a framework in which the simple genetic algorithm (SGA) is a special case. The framework is used to illuminate relationships between seemingly different
Hyperplane Ranking in Simple Genetic Algorithms
 PROC. OF THE 6TH INT'L. CONF. ON GAS
, 1995
"... We examine the role of hyperplane ranking during genetic search by developing a metric for measuring the degree of ranking that exists with respect to static hyperplane averages taken directly from the function, as well as the dynamic ranking of hyperplanes during genetic search. The metric ap ..."
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Cited by 8 (5 self)
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applied to static rankings subsumes the concept of deception but the metric provides a more precise characterization of a function. We show that the degree of dynamic ranking induced by a simple genetic algorithm is highly correlated with the degree of static ranking that is inherent
An Executable Model of a Simple Genetic Algorithm
 Foundations of Genetic Algorithms 2
, 1992
"... A set of executable equations are defined which model the ideal behavior of a simple genetic algorithm. The equations assume an infinitely large population and require the enumeration of all points in the search space. ..."
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Cited by 61 (5 self)
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A set of executable equations are defined which model the ideal behavior of a simple genetic algorithm. The equations assume an infinitely large population and require the enumeration of all points in the search space.
An analysis of a simple genetic algorithm
 PROCEEDINGS OF THE FOURTH CONFERENCE ON GENETIC ALGORITHMS
, 1991
"... The rate of convergence and the structure of stable populations are studied for a simple, and yet nontrivial, family of genetic algorithms. ..."
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Cited by 23 (2 self)
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The rate of convergence and the structure of stable populations are studied for a simple, and yet nontrivial, family of genetic algorithms.
Modeling Simple Genetic Algorithms for Permutation Problems
 in Foundations of Genetic Algorithms
, 1995
"... An exact model of a simple genetic algorithm is developed for permutation based representations. Permutation based representations are used for scheduling problems and combinatorial problems such as the Traveling Salesman Problem. A remapping function is developed to remap the model to all permut ..."
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Cited by 10 (1 self)
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An exact model of a simple genetic algorithm is developed for permutation based representations. Permutation based representations are used for scheduling problems and combinatorial problems such as the Traveling Salesman Problem. A remapping function is developed to remap the model to all
Drift, Diffusion and Boltzmann Distribution in Simple Genetic Algorithm
 In Matzke, D. (Ed.) Proceedings of the Workshop on Physics and Computation. IEEE Computer
, 1992
"... This paper presents a general diffusion model of a simple genetic algorithm. Unlike the similar previous efforts made for modeling mutation based genetic search, this work includes the effect of crossover by considering the dynamics of spatially averaged observables, under the influence of determini ..."
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Cited by 3 (1 self)
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This paper presents a general diffusion model of a simple genetic algorithm. Unlike the similar previous efforts made for modeling mutation based genetic search, this work includes the effect of crossover by considering the dynamics of spatially averaged observables, under the influence
Towards computing the parameters of the Simple Genetic Algorithm
"... The problem of finding appropriate probablilities for crossover and mutation with respect to resampling may be addressed using the Markov chain model. Our efforts in this direction lead through a simplification of the mixing matrix incorporating both probabilities. In the paper we present the simpli ..."
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the simplification and discuss some of its ramifications. We expect that it may lead to some improvement of the computational properties of the Markov chain model of the Simple Genetic Algorithm.
A Markov Framework for the Simple Genetic Algorithm
 Evolutionary Computation
, 1993
"... This paper develops a theoretical framework based on Markov chains for the simple genetic algorithm (operators of reproduction, crossover, and mutation). We prove the existence of a unique stationary distribution for the Markov chain when mutation probability is used as a control parameter. We also ..."
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Cited by 3 (0 self)
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This paper develops a theoretical framework based on Markov chains for the simple genetic algorithm (operators of reproduction, crossover, and mutation). We prove the existence of a unique stationary distribution for the Markov chain when mutation probability is used as a control parameter. We also
Results 1  10
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