Classification And Learning Using Genetic Algorithms
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Classification And Learning Using Genetic Algorithms
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Author : Sanghamitra Bandyopadhyay
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-05-17
Classification And Learning Using Genetic Algorithms written by Sanghamitra Bandyopadhyay 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 2007-05-17 with Computers categories.
This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks.
Learning Concept Classification Rules Using Genetic Algorithms
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Author : Kenneth A. Dejong
language : en
Publisher:
Release Date : 1991
Learning Concept Classification Rules Using Genetic Algorithms written by Kenneth A. Dejong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with categories.
Genetic Algorithms And Genetic Programming
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Author : Michael Affenzeller
language : en
Publisher: CRC Press
Release Date : 2009-04-09
Genetic Algorithms And Genetic Programming written by Michael Affenzeller and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-09 with Computers categories.
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimization problems and describes structure identification using HeuristicLab as a platform for al
Learning Concept Classification Rules Using Genetic Algorithms
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Author : Kenneth A. Dejong
language : en
Publisher:
Release Date : 1991
Learning Concept Classification Rules Using Genetic Algorithms written by Kenneth A. Dejong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with categories.
Genetic Algorithms Fuzzy Systems And Website Classification
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Author : Rafiqul Islam
language : en
Publisher:
Release Date : 2012
Genetic Algorithms Fuzzy Systems And Website Classification written by Rafiqul Islam and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Adaptive control systems categories.
This book presents and discusses current research in the study of genetic algorithms, fuzzy systems and website classification. Topics discussed include genetic algorithm for optimal design of fuzzy classifiers; design and analysis of type-2 fuzzy PI controller; selection of supply chain through fuzzy outranking techniques; fast web page classification without accessing the web page using machine learning techniques; classification algorithms in handling noisy training data and meta data generation for automates web page classification.
Genetic Algorithms In Applications
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Author : Rustem Popa
language : en
Publisher: BoD – Books on Demand
Release Date : 2012-03-21
Genetic Algorithms In Applications written by Rustem Popa and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-21 with Computers categories.
Genetic Algorithms (GAs) are one of several techniques in the family of Evolutionary Algorithms - algorithms that search for solutions to optimization problems by "evolving" better and better solutions. Genetic Algorithms have been applied in science, engineering, business and social sciences. This book consists of 16 chapters organized into five sections. The first section deals with some applications in automatic control, the second section contains several applications in scheduling of resources, and the third section introduces some applications in electrical and electronics engineering. The next section illustrates some examples of character recognition and multi-criteria classification, and the last one deals with trading systems. These evolutionary techniques may be useful to engineers and scientists in various fields of specialization, who need some optimization techniques in their work and who may be using Genetic Algorithms in their applications for the first time. These applications may be useful to many other people who are getting familiar with the subject of Genetic Algorithms.
A Genetic Programming Approach To Classification Problems
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Author : Hakan Uysal
language : en
Publisher: GRIN Verlag
Release Date : 2016-07-26
A Genetic Programming Approach To Classification Problems written by Hakan Uysal and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-26 with Computers categories.
Essay from the year 2013 in the subject Computer Science - Programming, grade: A+, University College Dublin, course: Natural Computing, language: English, abstract: Genetic Programming is a biological evolution inspired technique for computer programs to solve problems automatically by evolving iteratively using a fitness function. The advantage of this type programming is that it only defines the basics. As a result of this, it is a flexible solution for broad range of domains. Classification has been one of the most compelling problems in machine learning. In this paper, there is a comparison between genetic programming classifier and conventional classification algorithms like Naive Bayes, C4.5 decision tree, Random Forest, Support Vector Machines and k-Nearest Neighbour. The experiment is done on several data sets with different sizes, feature sets and attribute properties. There is also an experiment on the time complexity of each classifier method.
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.
Deep Learning Using Genetic Algorithms
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Author : Joshua D. Lamos-Sweeney
language : en
Publisher:
Release Date : 2012
Deep Learning Using Genetic Algorithms written by Joshua D. Lamos-Sweeney and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Neural networks (Computer science) categories.
"Deep Learning networks are a new type of neural network that discovers important object features. These networks determine features without supervision, and are adept at learning high level abstractions about their data sets. These networks are useful for a variety of tasks, but are difficult to train. This difficulty is compounded when multiple networks are trained in a layered fashion, which results in increased solution complexity as well as increased training time. This paper examines the use of Genetic Algorithms as a training mechanism for Deep Learning networks, with emphasis on training networks with a large number of layers, each of which is trained independently to reduce the computational burden and increase the overall flexibility of the algorithm. This paper covers the implementation of a multilayer deep learning network using a genetic algorithm, including tuning the genetic algorithm, as well as results of experiments involving data compression and object classification. This paper aims to show that a genetic algorithm can be used to train a non trivial deep learning network in place of existing methodologies for network training, and that the features extracted can be used for a variety of real world computational problems."--Abstract.
Hands On Genetic Algorithms With Python
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Author : Eyal Wirsansky
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-07-12
Hands On Genetic Algorithms With Python written by Eyal Wirsansky and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-12 with Computers categories.
Explore the ever-growing world of genetic algorithms to build and enhance AI applications involving search, optimization, machine learning, deep learning, NLP, and XAI using Python libraries Key Features Learn how to implement genetic algorithms using Python libraries DEAP, scikit-learn, and NumPy Take advantage of cloud computing technology to increase the performance of your solutions Discover bio-inspired algorithms such as particle swarm optimization (PSO) and NEAT Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWritten by Eyal Wirsansky, a senior data scientist and AI researcher with over 25 years of experience and a research background in genetic algorithms and neural networks, Hands-On Genetic Algorithms with Python offers expert insights and practical knowledge to master genetic algorithms. After an introduction to genetic algorithms and their principles of operation, you’ll find out how they differ from traditional algorithms and the types of problems they can solve, followed by applying them to search and optimization tasks such as planning, scheduling, gaming, and analytics. As you progress, you’ll delve into explainable AI and apply genetic algorithms to AI to improve machine learning and deep learning models, as well as tackle reinforcement learning and NLP tasks. This updated second edition further expands on applying genetic algorithms to NLP and XAI and speeding up genetic algorithms with concurrency and cloud computing. You’ll also get to grips with the NEAT algorithm. The book concludes with an image reconstruction project and other related technologies for future applications. By the end of this book, you’ll have gained hands-on experience in applying genetic algorithms across a variety of fields, with emphasis on artificial intelligence with Python.What you will learn Use genetic algorithms to solve planning, scheduling, gaming, and analytics problems Create reinforcement learning, NLP, and explainable AI applications Enhance the performance of ML models and optimize deep learning architecture Deploy genetic algorithms using client-server architectures, enhancing scalability and computational efficiency Explore how images can be reconstructed using a set of semi-transparent shapes Delve into topics like elitism, niching, and multiplicity in genetic solutions to enhance optimization strategies and solution diversity Who this book is for If you’re a data scientist, software developer, AI enthusiast who wants to break into the world of genetic algorithms and apply them to real-world, intelligent applications as quickly as possible, this book is for you. Working knowledge of the Python programming language is required to get started with this book.