Genetic Algorithms in Optimisation, Simulation and Modelling, (Frontiers in Artificial Intelligence and Applications , Vol 23)

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Genetic Algorithms in Optimisation, Simulation and Modelling, (Frontiers in Artificial Intelligence and Applications , Vol 23) Overview

This book examines the implementation and applications of genetic algorithms (GA) to the domain of AI.In recent years the trend towards, real world applications is fgaining ground especially in GA. The general purpose nature of GA is examined from an interdiciplinary point of view. Despite the differences that may exist in between representations across domain problems the commonality of in the design of GA is upheld. This work provides an overview of the current developments in Europe a section is devoted to the progrmamming of Parallel Genetic Algorithms (including GAME) and a section on Optimisation and Complex Modelling. Readers: researchers in AI, mathematics and computing.

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Many-Agent Simulation and Artificial Life (Frontiers in Artificial Intelligence and Applications)

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Many-Agent Simulation and Artificial Life (Frontiers in Artificial Intelligence and Applications) Overview

This work provides an overview of the theoretical, as well as the practical, aspects involved in the study and implementation of many-agent simulation models. The model presented here is based on the idea that programs exhibit behaviours entirely described by their internal mechanisms, namely the program instructions. By relating an individual to a program, it is possible to achieve an artificial world inhabited by interacting processes. It is possible, then, to achieve simulation by transposing the population of a real system to this artificial counterpart in which particular hypotheses can be explored by repeating experiments the same way as in a real laboratory, but more easily. Each member of a population is then represented as an agent whose behaviour is programmed with all the required details. Many-agent simulations therefore primarily help to model situations in which individuals have complex and different behaviours, while taking into account both quantitative (numerical parameters) and qualitative (individual behaviours) properties of the system.

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Computational Neuroscience: Realistic Modeling for Experimentalists (Frontiers in Neuroscience)

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Computational Neuroscience: Realistic Modeling for Experimentalists (Frontiers in Neuroscience) Overview

Designed primarily as an introduction to realistic modeling methods, Computational Neuroscience: Realistic Modeling for Experimentalists focuses on methodological approaches, selecting appropriate methods, and identifying potential pitfalls. The author addresses varying levels of complexity, from molecular interactions within single neurons to the processing of information by neural networks. He avoids theoretical mathematics and provides just enough of the basic math used by experimentalists.What makes this resource unique is the inclusion of a CD-ROM that furnishes interactive modeling examples. It contains tutorials and demos, movies and images, and the simulation scripts necessary to run the full simulation described in the chapter examples. Each chapter covers: the theoretical foundation; parameters needed; appropriate software descriptions; evaluation of the model; future directions expected; examples in text boxes linked to the CD-ROM; and references. The first book to bring you cutting-edge developments in neuronal modeling. It provides an introduction to realistic modeling methods at levels of complexity varying from molecular interactions to neural networks. The book and CD-ROM combine to make Computational Neuroscience: Realistic Modeling for Experimentalists the complete package for understanding modeling techniques.

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• Computational Modeling Methods for Neuroscientists (Computational Neuroscience)
• Signal Processing for Neuroscientists: An Introduction to the Analysis of Physiological Signals
• Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
• Tutorial on Neural Systems Modeling
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