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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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Genetic Algorithm Against Cancer
2006We present an evolutionary approach to the search for effective vaccination schedules using mathematical computerized model as a fitness evaluator. Our study is based on our previous model that simulates the Cancer – Immune System competition activated by a tumor vaccine. The model reproduces pre-clinical results obtained for an immunoprevention cancer
PAPPALARDO, FRANCESCO +3 more
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Genetic algorithms in chemistry
Journal of Chromatography A, 2007Genetic algorithms (GAs) are a quite recent technique of optimization, whose basic concept is mimicking the evolution of a species, according to the Darwinian theory of the "survival of the fittest." The application of genetic algorithms to complex problems usually produces much better results than those obtained by the standard techniques.
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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
Bernd Freisleben, Michael Härtfelder
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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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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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Putting More Genetics into Genetic Algorithms
Evolutionary Computation, 1998The majority of current genetic algorithms (GAs), while inspired by natural evolutionary systems, are seldom viewed as biologically plausible models. This is not a criticism of GAs, but rather a reflection of choices made regarding the level of abstraction at which biological mechanisms are modeled, and a reflection of the more engineering-oriented ...
D S, Burke +4 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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Noisy intermediate-scale quantum algorithms
Reviews of Modern Physics, 2022Kishor Bharti +2 more
exaly