• Publications
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The compact genetic algorithm
TLDR
Introduces the compact genetic algorithm (cGA) which represents the population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. Expand
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A Survey of Optimization by Building and Using Probabilistic Models
TLDR
This paper summarizes the research on population-based probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the exploration of the search space. Expand
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A parameter-less genetic algorithm
TLDR
We propose a parameter-less genetic algorithm that relieves the user from having to set the parameters of the GA. Expand
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Parameter Setting in Evolutionary Algorithms
TLDR
This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. Expand
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Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA)
TLDR
This chapter explores the relationship between the linkage-learning problem and that of learning probability distributions over multi-variate spaces. Expand
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Decision making in a hybrid genetic algorithm
  • F. Lobo, D. Goldberg
  • Computer Science
  • Proceedings of IEEE International Conference on…
  • 13 April 1997
TLDR
We present a model for hybridizing genetic algorithms (GAs) based on a concept that decision theorists call probability matching and we use it to combine an elitist selecto-recombinative GA with a simple hill climber (HC). Expand
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Influence of selection and replacement strategies on linkage learning in BOA
TLDR
The Bayesian optimization algorithm (BOA) uses Bayesian networks to learn linkages between the decision variables of an optimization problem. Expand
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Multi-objective evolutionary algorithm for land-use management problem
TLDR
Land-use management problem/practice may be defined as the process of allocating different competitive land uses/activities, such as agriculture, forest, industries, recreational activities or conservation, to different units of a landscape to meet the desired objectives of land managers. Expand
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The parameter-less genetic algorithm in practice
TLDR
This paper shows how the parameter-less genetic algorithm can be used in practice by applying it to a network expansion problem. Expand
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Time Complexity of genetic algorithms on exponentially scaled problems
TLDR
This paper gives a theoretical and empirical analysis of the time complexity of genetic algorithms (GAs) on problems with exponentially scaled building blocks. Expand
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