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IEE Colloquium on Applications of Genetic Algorithms

15 Mar 1994

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  • GAME parallel support strategies for the parallelisation of genetic algorithms

    Publication Year: 1994, Page(s):4/1 - 4/4
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (303 KB)

    The parallelism is introduced in the applications classified as time consuming ones to improve their performance measured generally by the execution time. Many studies and experiences have shown that the introduction of the parallelism in the genetic application improves the quality of the result as well as the execution time. This is a consequence of the optimisation technique used in the GA whic... View full abstract»

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  • The Breeder Genetic Algorithm-a provable optimal search algorithm and its application

    Publication Year: 1994, Page(s):5/1 - 5/3
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (234 KB)

    Evolution of natural organisms is based on three major components-reproduction, variation and selection. Some reproductions of natural organisms occur with 'failures' called mutations. A more systematic variation of the genetic material happens in sexual reproduction. Each parent contributes half of its genetic material to the offspring. This method of variation is called recombination. The offspr... View full abstract»

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  • Genetic algorithms for protein tertiary structure prediction

    Publication Year: 1994, Page(s):6/1 - 6/5
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (369 KB)

    A genetic algorithm is used to search energetically and structurally favorable conformations. We use a hybrid protein representation, three operators to manipulate the protein 'genes', and a fitness function based on a simple force field. The prototype was applied to the ab initio prediction of Crambin. None of the conformations generated with a non-biased fitness function are similar to the nativ... View full abstract»

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  • Sex between models-inductive modelling using genetic algorithms

    Publication Year: 1994, Page(s):7/1 - 7/3
    Cited by:  Papers (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (296 KB)

    We describe GAAF (Genetic Algorithm for the Approximation of Formulae) which is a tool that, given a database of examples, will induce a model that explains those examples. GAAF was developed by Cap Volmac, a member of the Cap Gemini Sogeti Group, as part of the ESPRIT III project PAPAGENA (6857). GAAF is primarily targeted at the financial market. As such it forms the core of the OMEGA system tha... View full abstract»

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  • IEE Colloquium on 'Applications of Genetic Algorithms' (Digest No.1994/067)

    Publication Year: 1994
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (169 KB)

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  • The GAME system (Genetic Algorithms Manipulation Environment)

    Publication Year: 1994, Page(s):2/1 - 2/4
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (341 KB)

    The use of the genetic algorithms (GAs) for solving complex optimisation problems has increased in the last couple of years. A number of research groups and companies in the US and Europe have produced software tool kits to help with the development of GA based applications. Although exhibiting intrinsic parallelism, on searching for the optimal solution, Genetic Algorithms' computational models a... View full abstract»

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  • Introduction to genetic algorithms

    Publication Year: 1994, Page(s):1/1 - 1/4
    Cited by:  Papers (13)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (272 KB)

    Genetic algorithms in general, and parallel genetic algorithms in particular, are of major significance to the development of the new generation of IT applications. The potential which parallel genetic algorithms offer over existing information processing techniques is enormous. Genetic algorithms are ideally suited to the processing, classification and control of very-large and varied data. There... View full abstract»

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  • Development needs for diverse genetic algorithm design

    Publication Year: 1994, Page(s):3/1 - 311
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (770 KB)

    This paper describes the development of an object-oriented parallel programming environment for genetic algorithms. This work, carried out as part of the ESPRIT III initiative PAPAGENA, intends to promote, develop and demonstrate the effectiveness of genetic algorithm (GA) and parallel genetic algorithm (PGA) techniques in a variety of real-world application domains. Central to this task is the de... View full abstract»

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