Adaptive Learning By Genetic Algorithms
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Adaptive Learning By Genetic Algorithms
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Author : Herbert Dawid
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Adaptive Learning By Genetic Algorithms written by Herbert Dawid and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Business & Economics categories.
An analysis of the learning behavior of genetic algorithms in economic systems with mutual interaction, such as markets. These systems are characterized by a state-dependent fitness function and - for the first time - mathematical results characterizing the long-term outcome of genetic learning in such systems are provided. The usefulness of such results is illustrated by many simulations in evolutionary games and economic models.
Adaptive Learning By Genetic Algorithms
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Author : Herbert Dawid
language : en
Publisher:
Release Date : 1996-09-13
Adaptive Learning By Genetic Algorithms written by Herbert Dawid and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-09-13 with categories.
Adaptive Learning Of Polynomial Networks
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Author : Nikolay Nikolaev
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-08-18
Adaptive Learning Of Polynomial Networks written by Nikolay Nikolaev and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-08-18 with Computers categories.
This book delivers theoretical and practical knowledge for developing algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The text emphasizes an organized model identification process by which to discover models that generalize and predict well. The book further facilitates the discovery of polynomial models for time-series prediction.
Evolutionary Learning Algorithms For Neural Adaptive Control
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Author : Dimitris C. Dracopoulos
language : en
Publisher: Springer
Release Date : 2013-12-21
Evolutionary Learning Algorithms For Neural Adaptive Control written by Dimitris C. Dracopoulos and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-21 with Computers categories.
Evolutionary Learning Algorithms for Neural Adaptive Control is an advanced textbook, which investigates how neural networks and genetic algorithms can be applied to difficult adaptive control problems which conventional results are either unable to solve , or for which they can not provide satisfactory results. It focuses on the principles involved, rather than on the modelling of the applications themselves, and therefore provides the reader with a good introduction to the fundamental issues involved.
Learning Genetic Algorithms With Python
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Author : Ivan Gridin
language : en
Publisher: BPB Publications
Release Date : 2021-02-13
Learning Genetic Algorithms With Python written by Ivan Gridin and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-13 with Computers categories.
Refuel your AI Models and ML applications with High-Quality Optimization and Search Solutions DESCRIPTION Genetic algorithms are one of the most straightforward and powerful techniques used in machine learning. This book ÔLearning Genetic Algorithms with PythonÕ guides the reader right from the basics of genetic algorithms to its real practical implementation in production environments.Ê Each of the chapters gives the reader an intuitive understanding of each concept. You will learn how to build a genetic algorithm from scratch and implement it in real-life problems. Covered with practical illustrated examples, you will learn to design and choose the best model architecture for the particular tasks. Cutting edge examples like radar and football manager problem statements, you will learn to solve high-dimensional big data challenges with ways of optimizing genetic algorithms. KEY FEATURESÊÊ _ Complete coverage on practical implementation of genetic algorithms. _ Intuitive explanations and visualizations supply theoretical concepts. _ Added examples and use-cases on the performance of genetic algorithms. _ Use of Python libraries and a niche coverage on the performance optimization of genetic algorithms. WHAT YOU WILL LEARNÊ _ Understand the mechanism of genetic algorithms using popular python libraries. _ Learn the principles and architecture of genetic algorithms. _ Apply and Solve planning, scheduling and analytics problems in Enterprise applications. _Ê Expert learning on prime concepts like Selection, Mutation and Crossover. WHO THIS BOOK IS FORÊÊ The book is for Data Science team, Analytics team, AI Engineers, ML Professionals who want to integrate genetic algorithms to refuel their ML and AI applications. No special expertise about machine learning is required although a basic knowledge of Python is expected. TABLE OF CONTENTS 1. Introduction 2. Genetic Algorithm Flow 3. Selection 4. Crossover 5. Mutation 6. Effectiveness 7. Parameter Tuning 8. Black-box Function 9. Combinatorial Optimization: Binary Gene Encoding 10. Combinatorial Optimization: Ordered Gene Encoding 11. Other Common Problems 12. Adaptive Genetic Algorithm 13. Improving Performance
Genetic Algorithms And Their Applications
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Author : John J. Grefenstette
language : en
Publisher: Psychology Press
Release Date : 2013-08-21
Genetic Algorithms And Their Applications written by John J. Grefenstette and has been published by Psychology Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-21 with Psychology categories.
First Published in 1987. This is the collected proceedings of the second International Conference on Genetic Algorithms held at the Massachusetts Institute of Technology, Cambridge, MA on the 28th to the 31st July 1987. With papers on Genetic search theory, Adaptive search operators, representation issues, connectionism and parallelism, credit assignment ad learning, and applications.
Anticipatory Behavior In Adaptive Learning Systems
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Author : Martin V. Butz
language : en
Publisher: Springer
Release Date : 2004-01-21
Anticipatory Behavior In Adaptive Learning Systems written by Martin V. Butz and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-01-21 with Computers categories.
The interdisciplinary topic of anticipation, attracting attention fromnbsp;computer scientists, psychologists, philosophers, neuroscientists, and biologists is a rather new and often misunderstood matter of research. This book attempts to establish anticipation as a research topic and encourage further research and development work. First, the book presents philosophical thoughts and concepts to stimulate the reader's concern about the topic. Fundamental cognitive psychology experiments then confirm the existence of anticipatory behavior in animals and humans and outline a first framework of anticipatory learning and behavior. Next, several distinctions and frameworks of anticipatory processes are discussed, including first implementations of these concepts. Finally, several anticipatory systems and studies on anticipatory behavior are presented.
Adaptive Micro Learning Using Fragmented Time To Learn
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Author : Geng Sun
language : en
Publisher: World Scientific
Release Date : 2020-02-18
Adaptive Micro Learning Using Fragmented Time To Learn written by Geng Sun and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-18 with Computers categories.
This compendium introduces an artificial intelligence-supported solution to realize adaptive micro learning over open education resource (OER). The advantages of cloud computing and big data are leveraged to promote the categorization and customization of OERs micro learning context. For a micro-learning service, OERs are tailored into fragmented pieces to be consumed within shorter time frames.Firstly, the current status of mobile-learning, micro-learning, and OERs are described. Then, the significances and challenges of Micro Learning as a Service (MLaaS) are discussed. A framework of a service-oriented system is provided, which adopts both online and offline computation domain to work in conjunction to improve the performance of learning resource adaptation.In addition, a comprehensive learner model and a knowledge base is prepared to semantically profile the learners and learning resource. The novel delivery and access mode of OERs suffers from the cold start problem because of the shortage of already-known learner information versus the continuously released new micro OERs. This unique volume provides an excellent feasible algorithmic solution to overcome the cold start problem.
Evolutionary Computation
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Author : Xin Yao
language : en
Publisher: World Scientific
Release Date : 1999
Evolutionary Computation written by Xin Yao and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Science categories.
Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation. This book is devoted to the theory and application of evolutionary computation. It is a self-contained volume which covers both introductory material and selected advanced topics. The book can roughly be divided into two major parts: the introductory one and the one on selected advanced topics. Each part consists of several chapters which present an in-depth discussion of selected topics. A strong connection is established between evolutionary algorithms and traditional search algorithms. This connection enables us to incorporate ideas in more established fields into evolutionary algorithms. The book is aimed at a wide range of readers. It does not require previous exposure to the field since introductory material is included. It will be of interest to anyone who is interested in adaptive optimization and learning. People in computer science, artificial intelligence, operations research, and various engineering fields will find it particularly interesting.
Genetic Algorithms For Machine Learning
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Author : John J. Grefenstette
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Genetic Algorithms For Machine Learning written by John J. Grefenstette and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
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.