Evolutionary Artificial Intelligence
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Evolutionary Machine Learning Techniques
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Author : Seyedali Mirjalili
language : en
Publisher: Springer Nature
Release Date : 2019-11-11
Evolutionary Machine Learning Techniques written by Seyedali Mirjalili and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-11 with Technology & Engineering categories.
This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.
Artificial Intelligence And Evolutionary Algorithms In Engineering Systems
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Author : L. Padma Suresh
language : en
Publisher: Springer
Release Date : 2014-11-01
Artificial Intelligence And Evolutionary Algorithms In Engineering Systems written by L. Padma Suresh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-01 with Technology & Engineering categories.
The book is a collection of high-quality peer-reviewed research papers presented in Proceedings of International Conference on Artificial Intelligence and Evolutionary Algorithms in Engineering Systems (ICAEES 2014) held at Noorul Islam Centre for Higher Education, Kumaracoil, India. These research papers provide the latest developments in the broad area of use of artificial intelligence and evolutionary algorithms in engineering systems. The book discusses wide variety of industrial, engineering and scientific applications of the emerging techniques. It presents invited papers from the inventors/originators of new applications and advanced technologies.
Evolutionary Optimization Algorithms
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Author : Dan Simon
language : en
Publisher: John Wiley & Sons
Release Date : 2013-06-13
Evolutionary Optimization Algorithms written by Dan Simon and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-13 with Mathematics categories.
A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear but theoretically rigorous understanding of evolutionary algorithms, with an emphasis on implementation Gives a careful treatment of recently developed EAs including opposition-based learning, artificial fish swarms, bacterial foraging, and many others and discusses their similarities and differences from more well-established EAs Includes chapter-end problems plus a solutions manual available online for instructors Offers simple examples that provide the reader with an intuitive understanding of the theory Features source code for the examples available on the author's website Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.
Illustrating Evolutionary Computation With Mathematica
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Author : Christian Jacob
language : en
Publisher: Elsevier
Release Date : 2001-02-23
Illustrating Evolutionary Computation With Mathematica written by Christian Jacob and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-02-23 with Computers categories.
An essential capacity of intelligence is the ability to learn. An artificially intelligent system that could learn would not have to be programmed for every eventuality; it could adapt to its changing environment and conditions just as biological systems do. Illustrating Evolutionary Computation with Mathematica introduces evolutionary computation to the technically savvy reader who wishes to explore this fascinating and increasingly important field. Unique among books on evolutionary computation, the book also explores the application of evolution to developmental processes in nature, such as the growth processes in cells and plants. If you are a newcomer to the evolutionary computation field, an engineer, a programmer, or even a biologist wanting to learn how to model the evolution and coevolution of plants, this book will provide you with a visually rich and engaging account of this complex subject.* Introduces the major mechanisms of biological evolution.* Demonstrates many fascinating aspects of evolution in nature with simple, yet illustrative examples.* Explains each of the major branches of evolutionary computation: genetic algorithms, genetic programming, evolutionary programming, and evolution strategies.* Demonstrates the programming of computers by evolutionary principles using Evolvica, a genetic programming system designed by the author.* Shows in detail how to evolve developmental programs modeled by cellular automata and Lindenmayer systems.* Provides Mathematica notebooks on the Web that include all the programs in the book and supporting animations, movies, and graphics.
Introduction To Evolutionary Computing
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Author : A.E. Eiben
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-08-06
Introduction To Evolutionary Computing written by A.E. Eiben 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 2007-08-06 with Computers categories.
The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.
Evolutionary Artificial Intelligence
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Author : David Asirvatham
language : en
Publisher: Springer Nature
Release Date : 2024-03-13
Evolutionary Artificial Intelligence written by David Asirvatham and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-13 with Computers categories.
This book gathers a collection of selected works and new research results of scholars and graduate students presented at International Conference on Evolutionary Artificial Intelligence (ICEAI 2023) held in Malaysia during 13-14 September 2023. The focus of the book is interdisciplinary in nature and includes research on all aspects of evolutionary computation to find effective solutions to a wide range of computationally difficult problems. The book covers topics such as particle swarm optimization, evolutionary programming, genetic programming, hybrid evolutionary algorithms, ant colony optimization, evolutionary neural networks, evolutionary reinforcement learning, genetic algorithms, memetic algorithms, novel bio-inspired algorithms, evolving multi-agent systems, agent-based evolutionary approaches, and evolutionary game theory.
Evolutionary Computation
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Author : David B. Fogel
language : en
Publisher: John Wiley & Sons
Release Date : 2006-01-03
Evolutionary Computation written by David B. Fogel and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-01-03 with Technology & Engineering categories.
This Third Edition provides the latest tools and techniques that enable computers to learn The Third Edition of this internationally acclaimed publication provides the latest theory and techniques for using simulated evolution to achieve machine intelligence. As a leading advocate for evolutionary computation, the author has successfully challenged the traditional notion of artificial intelligence, which essentially programs human knowledge fact by fact, but does not have the capacity to learn or adapt as evolutionary computation does. Readers gain an understanding of the history of evolutionary computation, which provides a foundation for the author's thorough presentation of the latest theories shaping current research. Balancing theory with practice, the author provides readers with the skills they need to apply evolutionary algorithms that can solve many of today's intransigent problems by adapting to new challenges and learning from experience. Several examples are provided that demonstrate how these evolutionary algorithms learn to solve problems. In particular, the author provides a detailed example of how an algorithm is used to evolve strategies for playing chess and checkers. As readers progress through the publication, they gain an increasing appreciation and understanding of the relationship between learning and intelligence. Readers familiar with the previous editions will discover much new and revised material that brings the publication thoroughly up to date with the latest research, including the latest theories and empirical properties of evolutionary computation. The Third Edition also features new knowledge-building aids. Readers will find a host of new and revised examples. New questions at the end of each chapter enable readers to test their knowledge. Intriguing assignments that prepare readers to manage challenges in industry and research have been added to the end of each chapter as well. This is a must-have reference for professionals in computer and electrical engineering; it provides them with the very latest techniques and applications in machine intelligence. With its question sets and assignments, the publication is also recommended as a graduate-level textbook.
Fundamentals Of The New Artificial Intelligence
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Author : Toshinori Munakata
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-01-01
Fundamentals Of The New Artificial Intelligence written by Toshinori Munakata 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 2008-01-01 with Computers categories.
The book covers the most essential and widely employed material in each area, particularly the material important for real-world applications. Our goal is not to cover every latest progress in the fields, nor to discuss every detail of various techniques that have been developed. New sections/subsections added in this edition are: Simulated Annealing (Section 3.7), Boltzmann Machines (Section 3.8) and Extended Fuzzy if-then Rules Tables (Sub-section 5.5.3). Also, numerous changes and typographical corrections have been made throughout the manuscript. The Preface to the first edition follows. General scope of the book Artificial intelligence (AI) as a field has undergone rapid growth in diversification and practicality. For the past few decades, the repertoire of AI techniques has evolved and expanded. Scores of newer fields have been added to the traditional symbolic AI. Symbolic AI covers areas such as knowledge-based systems, logical reasoning, symbolic machine learning, search techniques, and natural language processing. The newer fields include neural networks, genetic algorithms or evolutionary computing, fuzzy systems, rough set theory, and chaotic systems.
Artificial Intelligence
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Author : Saswat Sarangi
language : en
Publisher: Taylor & Francis
Release Date : 2018-09-03
Artificial Intelligence written by Saswat Sarangi and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-03 with Political Science categories.
What will the future be? A dystopian landscape controlled by machines or a brave new world full of possibilities? Perhaps the answer lies with Artificial Intelligence (AI)—a phenomenon much beyond technology that has, continues to, and will shape lives in ways we do not understand yet. This book traces the evolution of AI in contemporary history. It analyses how AI is primarily being driven by "capital" as the only "factor of production" and its consequences for the global political economy. It further explores the dystopian prospect of mass unemployment by AI and takes up the ethical aspects of AI and its possible use in undermining natural and fundamental rights. A tract for the times, this volume will be a major intervention in an area that is heavily debated but rarely understood. It will be essential reading for researchers and students of digital humanities, politics, economics, science and technology studies, physics, and computer science. It will also be key reading for policy makers, cyber experts and bureaucrats.
Bio Inspired Artificial Intelligence
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Author : Dario Floreano
language : en
Publisher: MIT Press
Release Date : 2008-08-22
Bio Inspired Artificial Intelligence written by Dario Floreano and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-22 with Computers categories.
A comprehensive introduction to new approaches in artificial intelligence and robotics that are inspired by self-organizing biological processes and structures. New approaches to artificial intelligence spring from the idea that intelligence emerges as much from cells, bodies, and societies as it does from evolution, development, and learning. Traditionally, artificial intelligence has been concerned with reproducing the abilities of human brains; newer approaches take inspiration from a wider range of biological structures that that are capable of autonomous self-organization. Examples of these new approaches include evolutionary computation and evolutionary electronics, artificial neural networks, immune systems, biorobotics, and swarm intelligence—to mention only a few. This book offers a comprehensive introduction to the emerging field of biologically inspired artificial intelligence that can be used as an upper-level text or as a reference for researchers. Each chapter presents computational approaches inspired by a different biological system; each begins with background information about the biological system and then proceeds to develop computational models that make use of biological concepts. The chapters cover evolutionary computation and electronics; cellular systems; neural systems, including neuromorphic engineering; developmental systems; immune systems; behavioral systems—including several approaches to robotics, including behavior-based, bio-mimetic, epigenetic, and evolutionary robots; and collective systems, including swarm robotics as well as cooperative and competitive co-evolving systems. Chapters end with a concluding overview and suggested reading.