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Genetic Algorithms-a Tool for OR?
Journal of the Operational Research Society, 1996Summary: Compared with other metaheuristic techniques such as simulated annealing and tabu search, research into the use of genetic algorithms for the solution of OR problems is still in its infancy. This paper provides an introduction to genetic algorithms and their use in the solution of both classical and practical operational research problems ...
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A Genetic Engineering Approach to Genetic Algorithms
Evolutionary Computation, 2001We present an extension to the standard genetic algorithm (GA), which is based on concepts of genetic engineering. The motivation is to discover useful and harmful genetic materials and then execute an evolutionary process in such a way that the population becomes increasingly composed of useful genetic material and increasingly free of the harmful ...
J S, Gero, V, Kazakov
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An introduction to genetic algorithms
Sadhana, 1999Genetic algorithms (GAs) are search and optimization tools, which work differently compared to classical search and optimization methods. Because of their broad applicability, ease of use, and global perspective, GAs have been increasingly applied to various search and optimization problems in the recent past.
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An alternative Genetic Algorithm
2006This paper presents a new Genetic Algorithm (GA), called Alternative Genetic Algorithm (AGA) which has been defined to facilitate theoretical investigations. We have shown that both AGA and the usual GA (UGA) obey similar difference equations. However, theoretical investigations on the AGA are much simpler than on the UGA. For the AGA, we can derive as
Hesser, Jürgen, Männer, Reinhard
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ACM SIGAPL APL Quote Quad, 1996
This paper describes an application oriented approach to the genetic-algorithm technique. The software is implemented in APL2 and exploits availability of user defined operators and separation of general purpose and problem specific code. The paper presents results of applying genetic algorithms to solve real life problems of simulation and predicting ...
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This paper describes an application oriented approach to the genetic-algorithm technique. The software is implemented in APL2 and exploits availability of user defined operators and separation of general purpose and problem specific code. The paper presents results of applying genetic algorithms to solve real life problems of simulation and predicting ...
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Proceedings of the CUBE International Information Technology Conference, 2012
This paper discusses a case study of grammar induction. Grammar induction is the process of learning grammar from a set of training data of the positive (S+) and negative (S-) strings. An algorithm has been designed and implemented for the induction of context free grammar (CFG).
Hari Mohan Pandey +2 more
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This paper discusses a case study of grammar induction. Grammar induction is the process of learning grammar from a set of training data of the positive (S+) and negative (S-) strings. An algorithm has been designed and implemented for the induction of context free grammar (CFG).
Hari Mohan Pandey +2 more
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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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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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Clinical management of metastatic colorectal cancer in the era of precision medicine
Ca-A Cancer Journal for Clinicians, 2022, Davide Ciardiello, Giulia Martini
exaly
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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