Off The Line Performance


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Spatial Evolutionary Modeling


Spatial Evolutionary Modeling

Author: Roman M. Krzanowski

language: en

Publisher: Oxford University Press

Release Date: 2001-08-02


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Evolutionary models (e.g., genetic algorithms, artificial life), explored in other fields for the past two decades, are now emerging as an important new tool in GIS for a number of reasons. First, they are highly appropriate for modeling geographic phenomena. Secondly, geographical problems are often spatially separate (broken down into local or regional problems) and evolutionary algorithms can exploit this structure. Finally, the ability to store, manipulate, and visualize spatial data has increased to the point that space-time-attribute databases can be easily handled.

Genetic Algorithms for Machine Learning


Genetic Algorithms for Machine Learning

Author: John J. Grefenstette

language: en

Publisher: Springer Science & Business Media

Release Date: 2012-12-06


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The articles presented here were selected from preliminary versions presented at the International Conference on Genetic Algorithms in June 1991, as well as at a special Workshop on Genetic Algorithms for Machine Learning at the same Conference. Genetic algorithms are general-purpose search algorithms that use principles inspired by natural population genetics to evolve solutions to problems. The basic idea is to maintain a population of knowledge structure that represent candidate solutions to the problem of interest. The population evolves over time through a process of competition (i.e. survival of the fittest) and controlled variation (i.e. recombination and mutation). Genetic Algorithms for Machine Learning contains articles on three topics that have not been the focus of many previous articles on GAs, namely concept learning from examples, reinforcement learning for control, and theoretical analysis of GAs. It is hoped that this sample will serve to broaden the acquaintance of the general machine learning community with the major areas of work on GAs. The articles in this book address a number of central issues in applying GAs to machine learning problems. For example, the choice of appropriate representation and the corresponding set of genetic learning operators is an important set of decisions facing a user of a genetic algorithm. The study of genetic algorithms is proceeding at a robust pace. If experimental progress and theoretical understanding continue to evolve as expected, genetic algorithms will continue to provide a distinctive approach to machine learning. Genetic Algorithms for Machine Learning is an edited volume of original research made up of invited contributions by leading researchers.

Classic Cases in Neuropsychology, Volume II


Classic Cases in Neuropsychology, Volume II

Author: Chris Code

language: en

Publisher: Psychology Press

Release Date: 2013-01-11


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From a contemporary perspective, Classic Cases in Neuropsychology, Volume II reviews important and significant cases described in historical and modern literature where brain damage has been sustained. The single case study has always been of central importance to the discipline of neuropsychology. Cognitive neuropsychology and cognitive neurolinguistics search for universal structures in thought processes, and single patients are an important means to that end. The role of the single case study in the historical development of the field and its increasing contribution to contemporary work is therefore recognised as crucial. This follow-up to the successful Classic Cases in Neuropsychology (1996) brings together more of the important case investigations which have shaped the way we think about the relationships between brain, behaviour and cognition. The book includes cases from the rich history of neuropsychology as well as important contemporary case studies in the fields of memory, language and perception. Some of the cases described are rare, some are seminal in the field, many were the first of their type to be described and gave rise to new theories, and some are still controversial. As in the first volume, each chapter highlights the relevance of the case to the development of neuropsychology and discusses the theoretical implication of the findings. Classic Cases in Neuropsychology, Volume II will be essential reading for students and researchers alike in the fields of neuropsychology and neuroscience. It will also be of interest to speech and language pathologists, therapists and clinicians in this area.