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Genetic Algorithms in Search Optimization and Machine Learning
TLDR
From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Expand
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A niched Pareto genetic algorithm for multiobjective optimization
TLDR
We introduce the Niched Pareto GA as an algorithm for finding and maintaining a diverse "Pareto optimal population" on two artificial problems and an open problem in hydrosystems. Expand
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A Comparative Analysis of Selection Schemes Used in Genetic Algorithms
TLDR
This paper considers a number of selection schemes commonly used in modern genetic algorithms on the basis of solutions to deterministic difference or differential equations, which are verified through computer simulations. Expand
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Genetic Algorithms with Sharing for Multimodalfunction Optimization
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BOA: the Bayesian optimization algorithm
TLDR
In this paper, an algorithm based on the concepts of genetic algorithms that uses an estimation of a probability distribution of promising solutions in order to generate new candidate solutions is proposed. Expand
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The Design of Innovation: Lessons from and for Competent Genetic Algorithms
From the Publisher: The Design of Innovation illustrates how to design and implement competent genetic algorithms - genetic algorithms that solve hard problems quickly, reliably, and accurately -Expand
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Alleles, loci and the traveling salesman problem
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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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Genetic Algorithms and Machine Learning
TLDR
There is no a priori reason why machine learning must borrow from nature. Expand
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Messy Genetic Algorithms: Motivation, Analysis, and First Results
TLDR
This paper defines and explores a messy genetic algorithm (mGA). Expand
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