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Optimization of Genetic Algorithms by Genetic Algorithms
1993This paper presents an approach to determine the optimal Genetic Algorithm (GA), i.e. the most preferable type of genetic operators and their parameter settings, for a given problem. The basic idea is to consider the search for the best GA as an optimization problem and use another GA to solve it. As a consequence, a primary GA operates on a population
Michael Härtfelder, Bernd Freisleben
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Introduction to genetic algorithms
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2007The Introduction to Genetic Algorithms Tutorial is aimed at GECCO attendees with limited knowledge of genetic algorithms, and will start "at the beginning," describing first a "classical" genetic algorithm in terms of the biological principles on which it is loosely based, then present some of the fundamental results that describe its performance ...
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Proceedings of the international conference on APL '91, 1991
Genetic algorithms, invented by J. H. Holland, emulate biological evolution in the computer and try to build programs that can adapt by themselves to perform a given function. In some sense, they are analogous to neural networks, but there are important differences between them.
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Genetic algorithms, invented by J. H. Holland, emulate biological evolution in the computer and try to build programs that can adapt by themselves to perform a given function. In some sense, they are analogous to neural networks, but there are important differences between them.
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Smooth genetic algorithm [PDF]
Journal of Physics A: Mathematical and General, 1994 Summary: An existing family of genetic algorithms, which were designed with discrete and binary variables in mind, has been extended in this paper to handle truly continuous variables. Its close relationships with Monte Carlo methods, the simplex method, simulated annealing and other direct, i.e. derivative-free global optimization algorithms creates a
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2008
The methods in this chapter were developed in response to the need for general purpose methods for solving complex optimisation problems. A classical problem addressed is the Travelling Salesman Problem in which a salesman must visit each of n cities once and only once in an optimum order - that which minimises his travelling.
Darryl Charles +3 more
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The methods in this chapter were developed in response to the need for general purpose methods for solving complex optimisation problems. A classical problem addressed is the Travelling Salesman Problem in which a salesman must visit each of n cities once and only once in an optimum order - that which minimises his travelling.
Darryl Charles +3 more
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2015
The essence of machine learning is the search for the best solution to our problem: to find a classifier which classifies as correctly as possible not only the training examples, but also future examples. Chapter 1 explained the principle of one of the most popular AI-based search techniques, the so-called hill-climbing, and showed how it can be used ...
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The essence of machine learning is the search for the best solution to our problem: to find a classifier which classifies as correctly as possible not only the training examples, but also future examples. Chapter 1 explained the principle of one of the most popular AI-based search techniques, the so-called hill-climbing, and showed how it can be used ...
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The algebra of genetic algorithms [PDF]
Annals of Mathematics and Artificial Intelligence, 1994 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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2004
Publisher Summary This chapter reviews the basics of genetic algorithms (GAs), briefly describes the schema theorem and the building block hypothesis, and explains feature selection based on GAs, as one of the most important applications of GAs. GAs differ from classical optimization and search procedures: (1) direct manipulation of a coding, (2 ...
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Publisher Summary This chapter reviews the basics of genetic algorithms (GAs), briefly describes the schema theorem and the building block hypothesis, and explains feature selection based on GAs, as one of the most important applications of GAs. GAs differ from classical optimization and search procedures: (1) direct manipulation of a coding, (2 ...
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European Journal of Operational Research, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yu-Chiun Chiou, Lawrence W. Lan
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yu-Chiun Chiou, Lawrence W. Lan
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Genetic algorithms and evolution
Journal of Theoretical Biology, 1990The genetic algorithm (GA) as developed by Holland (1975, Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press) is an optimization technique based on natural selection. We use a modified version of this technique to investigate which aspects of natural selection make it an efficient search procedure.
Alasdair I. Houston +4 more
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