Introduction of parallelism in geneticalgorithms improves the quality of the result as well as the execution time. This is due to the optimization technique which is not deterministic used in GAs. Parallelism can be ...
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Introduction of parallelism in geneticalgorithms improves the quality of the result as well as the execution time. This is due to the optimization technique which is not deterministic used in GAs. Parallelism can be introduced at different levels: coarse-grain parallelism, intermediate granularity parallelism and fine-grain parallelism. This parallelism is a platform independent one and is done with the use of a general purpose communication mechanism library. The port of GAME on a new parallel platform consists only in rewriting the communication mechanism library according to the system's operating system. GAME implementations show its portability and the interest generated by the use of Parallel geneticalgorithms.
genetic Algorithm for the Approximation of Formulae (GAAF) is a tool that will induce a model that explains a given database of examples. GAAF is targeted at the financial market and it forms the core of the OMEGA sys...
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genetic Algorithm for the Approximation of Formulae (GAAF) is a tool that will induce a model that explains a given database of examples. GAAF is targeted at the financial market and it forms the core of the OMEGA system that breeds predictive models for credit scoring, insurance or marketing purposes. OMEGA is a collaborative development of Cap Volmac and KiQ Business Solutions Ltd., a UK-based financial consultancy firm. GAAF offers some advantages over conventional techniques for data fitting like statistics or neural networks like 1) it makes the model explicit, 2) it does not presuppose a fixed format for the solution to be squashed into. The models induced by GAAF are both more predictive and more robust.
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 b...
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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 native conformation but all of them show a much better overall fitness than the native structure. If guided by r.m.s. deviation the native conformation was reproduced at 1.3 angstrom. Therefore, the genetic algorithm's search was successful but the fitness function was no good indicator for native structure. In a side chain placement experiment Crambin was reproduced at 1.86 angstrom r.m.s. deviation.
The proceedings contains 8 papers. Topics discussed include natural sciences computing, algorithms, learning systems, database systems, genetic engineering, large scale systems and mathematical models.
The proceedings contains 8 papers. Topics discussed include natural sciences computing, algorithms, learning systems, database systems, genetic engineering, large scale systems and mathematical models.
The proceedings contains 8 papers. Topics discussed include parallel computer systems, manipulators, real time systems, geneticalgorithms, software engineering, fault tolerant systems, large scale systems, systems an...
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The proceedings contains 8 papers. Topics discussed include parallel computer systems, manipulators, real time systems, geneticalgorithms, software engineering, fault tolerant systems, large scale systems, systems analysis, telecommunication networks, process control and automation.
This paper describes the development of an object-oriented parallel programming environment for geneticalgorithms. This work, carried out as part of the ESPRIT III initiative PAPAGENA, intends to promote, develop and...
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This paper describes the development of an object-oriented parallel programming environment for geneticalgorithms. 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 development of a general-purpose programming environment for both parallel and sequential geneticalgorithms. GAME (genetic Algorithm Manipulation Environment) will offer extensive tools for the design, configuration and monitoring of GA applications. This paper gives an overview of the design philosophy behind GAME, indicating the types of service and facilities the finished product will offer. Intrinsic to the design is the provision of an extensive multi-levelled GA-specific library, offering GA and PGA applications, algorithms and operators. This will allow application developers the facilities to rapidly customise, configure and test novel GA and PGA designs. To sketch the types of application to be housed in GAME, a description of the applications currently under development within this project is also included. These range from finance through economic modelling to protein structure prediction. Key design requirements for GAME are versatility, together with flexibility. For this reason GAME has been designed to run within both Sun OS and PC DOS operating system, with or without parallel support.
The paper describes the implementation of algorithms which attempt to provide optimization techniques for logic circuits and field programmable gate arrays (FPGAs). These geneticalgorithms are used to select, breed a...
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The paper describes the implementation of algorithms which attempt to provide optimization techniques for logic circuits and field programmable gate arrays (FPGAs). These geneticalgorithms are used to select, breed and test potential solutions to these networks and recommend the nearest optimal solution. In practice, recommended solutions causes considerable savings on circuit implementations as experimental results show and demonstrate.
From the contents:Neural networks – theory and applications: NNs (= neural networks) classifier on continuous data domains– quantum associative memory – a new class of neuron-like discrete filters to image processi...
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ISBN:
(数字)9783709163849
ISBN:
(纸本)9783211833643
From the contents:
Neural networks – theory and applications: NNs (= neural networks) classifier on continuous data domains– quantum associative memory – a new class of neuron-like discrete filters to image processing – modular NNs for improving generalisation properties – presynaptic inhibition modelling for image processing application – NN recognition system for a curvature primal sketch – NN based nonlinear temporal-spatial noise rejection system – relaxation rate for improving Hopfield network – Oja's NN and influence of the learning gain on its dynamics
geneticalgorithms – theory and applications: transposition: a biological-inspired mechanism to use with GAs (= geneticalgorithms) – GA for decision tree induction – optimising decision classifications using GAs – scheduling tasks with intertask communication onto multiprocessors by GAs – design of robust networks with GA – effect of degenerate coding on GAs – multiple traffic signal control using a GA – evolving musical harmonisation – niched-penalty approach for constraint handling in GAs – GA with dynamic population size – GA with dynamic niche clustering for multimodal function optimisation
Soft computing and uncertainty: self-adaptation of evolutionary constructed decision trees by information spreading – evolutionary programming of near optimal NNs
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