Evolutionary Approach to Machine Learning and Deep Neural Networks

Evolutionary Approach to Machine Learning and Deep Neural Networks
Author: Hitoshi Iba
Publsiher: Springer
Total Pages: 245
Release: 2018-06-15
Genre: Computers
ISBN: 9789811302008

Download Evolutionary Approach to Machine Learning and Deep Neural Networks Book in PDF, Epub and Kindle

This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields. Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution. The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.

Evolutionary Algorithms and Neural Networks

Evolutionary Algorithms and Neural Networks
Author: Seyedali Mirjalili
Publsiher: Springer
Total Pages: 156
Release: 2018-06-26
Genre: Technology & Engineering
ISBN: 9783319930251

Download Evolutionary Algorithms and Neural Networks Book in PDF, Epub and Kindle

This book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms. At first, the book offers a literature review of several well-regarded evolutionary algorithms, including particle swarm and ant colony optimization, genetic algorithms and biogeography-based optimization. It then proposes evolutionary version of several types of neural networks such as feed forward neural networks, radial basis function networks, as well as recurrent neural networks and multi-later perceptron. Most of the challenges that have to be addressed when training artificial neural networks using evolutionary algorithms are discussed in detail. The book also demonstrates the application of the proposed algorithms for several purposes such as classification, clustering, approximation, and prediction problems. It provides a tutorial on how to design, adapt, and evaluate artificial neural networks as well, and includes source codes for most of the proposed techniques as supplementary materials.

Evolutionary and Swarm Intelligence Algorithms

Evolutionary and Swarm Intelligence Algorithms
Author: Jagdish Chand Bansal,Pramod Kumar Singh,Nikhil R. Pal
Publsiher: Springer
Total Pages: 190
Release: 2018-06-06
Genre: Technology & Engineering
ISBN: 9783319913414

Download Evolutionary and Swarm Intelligence Algorithms Book in PDF, Epub and Kindle

This book is a delight for academics, researchers and professionals working in evolutionary and swarm computing, computational intelligence, machine learning and engineering design, as well as search and optimization in general. It provides an introduction to the design and development of a number of popular and recent swarm and evolutionary algorithms with a focus on their applications in engineering problems in diverse domains. The topics discussed include particle swarm optimization, the artificial bee colony algorithm, Spider Monkey optimization algorithm, genetic algorithms, constrained multi-objective evolutionary algorithms, genetic programming, and evolutionary fuzzy systems. A friendly and informative treatment of the topics makes this book an ideal reference for beginners and those with experience alike.

Evolutionary Computation Machine Learning and Data Mining in Bioinformatics

Evolutionary Computation  Machine Learning and Data Mining in Bioinformatics
Author: Elena Marchiori
Publsiher: Springer Science & Business Media
Total Pages: 302
Release: 2007-04-02
Genre: Computers
ISBN: 9783540717829

Download Evolutionary Computation Machine Learning and Data Mining in Bioinformatics Book in PDF, Epub and Kindle

This book constitutes the refereed proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2007, held in Valencia, Spain in April 2007, colocated with the Evo* 2007 events. The 28 revised full papers were carefully reviewed and selected from 60 submissions. Bringing experts in computer science together with experts in bioinformatics and the biological sciences resulted in contributions on fundamental and theoretical issues, along with a wide variety of papers dealing with different applications areas, such as biomarker discovery, cell simulation and modeling, ecological modeling, fluxomics, gene networks, biotechnology, metabolomics, microarray analysis, phylogenetics, protein interactions, proteomics, sequence analysis and alignment, as well as systems biology

Advances in Evolutionary Computing

Advances in Evolutionary Computing
Author: Ashish Ghosh,Shigeyoshi Tsutsui
Publsiher: Springer Science & Business Media
Total Pages: 1006
Release: 2012-12-06
Genre: Computers
ISBN: 9783642189654

Download Advances in Evolutionary Computing Book in PDF, Epub and Kindle

This book provides a collection of fourty articles containing new material on both theoretical aspects of Evolutionary Computing (EC), and demonstrating the usefulness/success of it for various kinds of large-scale real world problems. Around 23 articles deal with various theoretical aspects of EC and 17 articles demonstrate the success of EC methodologies. These articles are written by leading experts of the field from different countries all over the world.

Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development

Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development
Author: Sandeep Kumar,Anand Nayyar,Anand Paul
Publsiher: CRC Press
Total Pages: 146
Release: 2019-11-11
Genre: Computers
ISBN: 9781000726794

Download Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development Book in PDF, Epub and Kindle

Healthcare sector is characterized by difficulty, dynamism and variety. In 21st century, healthcare domain is surrounded by tons of challenges in terms of Disease detection, prevention, high costs, skilled technicians and better infrastructure. In order to handle these challenges, Intelligent Healthcare management technologies are required to play an effective role in improvising patient’s life. Healthcare organizations also need to continuously discover useful and actionable knowledge to gain insight from tons of data for various purposes for saving lives, reducing medical operations errors, enhancing efficiency, reducing costs and making the whole world a healthy world. Applying Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development is essential nowadays. The objective of this book is to highlight various Swarm Intelligence and Evolutionary Algorithms techniques for various medical issues in terms of Cancer Diagnosis, Brain Tumor, Diabetic Retinopathy, Heart disease as well as drug design and development. The book will act as one-stop reference for readers to think and explore Swarm Intelligence and Evolutionary Algorithms seriously for real-time patient diagnosis, as the book provides solutions to various complex diseases found critical for medical practitioners to diagnose in real-world. Key Features: Highlights the importance and applications of Swarm Intelligence and Evolutionary Algorithms in Healthcare industry. Elaborates Swarm Intelligence and Evolutionary Algorithms for Cancer Detection. In-depth coverage of computational methodologies, approaches and techniques based on Swarm Intelligence and Evolutionary Algorithms for detecting Brain Tumour including deep learning to optimize brain tumor diagnosis. Provides a strong foundation for Diabetic Retinopathy detection using Swarm and Evolutionary algorithms. Focuses on applying Swarm Intelligence and Evolutionary Algorithms for Heart Disease detection and diagnosis. Comprehensively covers the role of Swarm Intelligence and Evolutionary Algorithms for Drug Design and Discovery. The book will play a significant role for Researchers, Medical Practitioners, Healthcare Professionals and Industrial Healthcare Research and Development wings to conduct advanced research in Healthcare using Swarm Intelligence and Evolutionary Algorithms techniques.

Evolutionary Optimization Algorithms

Evolutionary Optimization Algorithms
Author: Dan Simon
Publsiher: John Wiley & Sons
Total Pages: 772
Release: 2013-06-13
Genre: Mathematics
ISBN: 9781118659502

Download Evolutionary Optimization Algorithms Book in PDF, Epub and Kindle

A clear and lucid bottom-up approach to the basic principlesof evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificialintelligence. EAs are motivated by optimization processes that weobserve in nature, such as natural selection, species migration,bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, andprogramming of evolutionary optimization algorithms. Featuredalgorithms include genetic algorithms, genetic programming, antcolony optimization, particle swarm optimization, differentialevolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists thereader in obtaining a clear—but theoreticallyrigorous—understanding of evolutionary algorithms, with anemphasis on implementation Gives a careful treatment of recently developedEAs—including opposition-based learning, artificial fishswarms, bacterial foraging, and many others— and discussestheir similarities and differences from more well-establishedEAs Includes chapter-end problems plus a solutions manual availableonline for instructors Offers simple examples that provide the reader with anintuitive understanding of the theory Features source code for the examples available on the author'swebsite Provides advanced mathematical techniques for analyzing EAs,including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspiredand Population-Based Approaches to Computer Intelligence is anideal text for advanced undergraduate students, graduate students,and professionals involved in engineering and computer science.

Evolutionary Computation

Evolutionary Computation
Author: David B. Fogel
Publsiher: John Wiley & Sons
Total Pages: 384
Release: 2006-01-03
Genre: Technology & Engineering
ISBN: 9780471749202

Download Evolutionary Computation Book in PDF, Epub and Kindle

This Third Edition provides the latest tools and techniques thatenable computers to learn The Third Edition of this internationally acclaimed publicationprovides the latest theory and techniques for using simulatedevolution to achieve machine intelligence. As a leading advocatefor evolutionary computation, the author has successfullychallenged the traditional notion of artificial intelligence, whichessentially programs human knowledge fact by fact, but does nothave the capacity to learn or adapt as evolutionary computationdoes. Readers gain an understanding of the history of evolutionarycomputation, which provides a foundation for the author's thoroughpresentation of the latest theories shaping current research.Balancing theory with practice, the author provides readers withthe skills they need to apply evolutionary algorithms that cansolve many of today's intransigent problems by adapting to newchallenges and learning from experience. Several examples areprovided that demonstrate how these evolutionary algorithms learnto solve problems. In particular, the author provides a detailedexample of how an algorithm is used to evolve strategies forplaying chess and checkers. As readers progress through the publication, they gain anincreasing appreciation and understanding of the relationshipbetween learning and intelligence. Readers familiar with theprevious editions will discover much new and revised material thatbrings the publication thoroughly up to date with the latestresearch, including the latest theories and empirical properties ofevolutionary computation. The Third Edition also features new knowledge-building aids.Readers will find a host of new and revised examples. New questionsat the end of each chapter enable readers to test their knowledge.Intriguing assignments that prepare readers to manage challenges inindustry and research have been added to the end of each chapter aswell. This is a must-have reference for professionals in computer andelectrical engineering; it provides them with the very latesttechniques and applications in machine intelligence. With itsquestion sets and assignments, the publication is also recommendedas a graduate-level textbook.

Illustrating Evolutionary Computation with Mathematica

Illustrating Evolutionary Computation with Mathematica
Author: Christian Jacob
Publsiher: Morgan Kaufmann
Total Pages: 578
Release: 2001
Genre: Computers
ISBN: 9781558606371

Download Illustrating Evolutionary Computation with Mathematica Book in PDF, Epub and Kindle

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.

Towards a New Evolutionary Computation

Towards a New Evolutionary Computation
Author: Jose A. Lozano,Pedro Larrañaga,Iñaki Inza,Endika Bengoetxea
Publsiher: Springer
Total Pages: 294
Release: 2006-01-21
Genre: Technology & Engineering
ISBN: 9783540324942

Download Towards a New Evolutionary Computation Book in PDF, Epub and Kindle

Estimation of Distribution Algorithms (EDAs) are a set of algorithms in the Evolutionary Computation (EC) field characterized by the use of explicit probability distributions in optimization. Contrarily to other EC techniques such as the broadly known Genetic Algorithms (GAs) in EDAs, the crossover and mutation operators are substituted by the sampling of a distribution previously learnt from the selected individuals. EDAs have experienced a high development that has transformed them into an established discipline within the EC field. This book attracts the interest of new researchers in the EC field as well as in other optimization disciplines, and that it becomes a reference for all of us working on this topic. The twelve chapters of this book can be divided into those that endeavor to set a sound theoretical basis for EDAs, those that broaden the methodology of EDAs and finally those that have an applied objective.

Evolutionary Optimization

Evolutionary Optimization
Author: Ruhul Sarker,Masoud Mohammadian,Xin Yao
Publsiher: Springer Science & Business Media
Total Pages: 418
Release: 2002-01-31
Genre: Business & Economics
ISBN: 9780792376545

Download Evolutionary Optimization Book in PDF, Epub and Kindle

The use of evolutionary computation techniques has grown considerably over the past several years. Over this time, the use and applications of these techniques have been further enhanced resulting in a set of computational intelligence (also known as modern heuristics) tools that are particularly adept for solving complex optimization problems. Moreover, they are characteristically more robust than traditional methods based on formal logics or mathematical programming for many real world OR/MS problems. Hence, evolutionary computation techniques have dealt with complex optimization problems better than traditional optimization techniques although they can be applied to easy and simple problems where conventional techniques work well. Clearly there is a need for a volume that both reviews state-of-the-art evolutionary computation techniques, and surveys the most recent developments in their use for solving complex OR/MS problems. This volume on Evolutionary Optimization seeks to fill this need. Evolutionary Optimization is a volume of invited papers written by leading researchers in the field. All papers were peer reviewed by at least two recognized reviewers. The book covers the foundation as well as the practical side of evolutionary optimization.

Hands On Genetic Algorithms with Python

Hands On Genetic Algorithms with Python
Author: Eyal Wirsansky
Publsiher: Packt Publishing Ltd
Total Pages: 346
Release: 2020-01-31
Genre: Computers
ISBN: 9781838559182

Download Hands On Genetic Algorithms with Python Book in PDF, Epub and Kindle

Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python libraries such as DEAP, scikit-learn, and NumPy Key Features Explore the ins and outs of genetic algorithms with this fast-paced guide Implement tasks such as feature selection, search optimization, and cluster analysis using Python Solve combinatorial problems, optimize functions, and enhance the performance of artificial intelligence applications Book Description Genetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution. By imitating the evolutionary process, genetic algorithms can overcome hurdles encountered in traditional search algorithms and provide high-quality solutions for a variety of problems. This book will help you get to grips with a powerful yet simple approach to applying genetic algorithms to a wide range of tasks using Python, covering the latest developments in artificial intelligence. After introducing you to genetic algorithms and their principles of operation, you'll understand how they differ from traditional algorithms and what types of problems they can solve. You'll then discover how they can be applied to search and optimization problems, such as planning, scheduling, gaming, and analytics. As you advance, you'll also learn how to use genetic algorithms to improve your machine learning and deep learning models, solve reinforcement learning tasks, and perform image reconstruction. Finally, you'll cover several related technologies that can open up new possibilities for future applications. By the end of this book, you'll have hands-on experience of applying genetic algorithms in artificial intelligence as well as in numerous other domains. What you will learn Understand how to use state-of-the-art Python tools to create genetic algorithm-based applications Use genetic algorithms to optimize functions and solve planning and scheduling problems Enhance the performance of machine learning models and optimize deep learning network architecture Apply genetic algorithms to reinforcement learning tasks using OpenAI Gym Explore how images can be reconstructed using a set of semi-transparent shapes Discover other bio-inspired techniques, such as genetic programming and particle swarm optimization Who this book is for This book is for software developers, data scientists, and AI enthusiasts who want to use genetic algorithms to carry out intelligent tasks in their applications. Working knowledge of Python and basic knowledge of mathematics and computer science will help you get the most out of this book.

Introduction to Evolutionary Algorithms

Introduction to Evolutionary Algorithms
Author: Xinjie Yu,Mitsuo Gen
Publsiher: Springer Science & Business Media
Total Pages: 422
Release: 2010-06-10
Genre: Computers
ISBN: 9781849961295

Download Introduction to Evolutionary Algorithms Book in PDF, Epub and Kindle

Evolutionary algorithms are becoming increasingly attractive across various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science and economics. Introduction to Evolutionary Algorithms presents an insightful, comprehensive, and up-to-date treatment of evolutionary algorithms. It covers such hot topics as: • genetic algorithms, • differential evolution, • swarm intelligence, and • artificial immune systems. The reader is introduced to a range of applications, as Introduction to Evolutionary Algorithms demonstrates how to model real world problems, how to encode and decode individuals, and how to design effective search operators according to the chromosome structures with examples of constraint optimization, multiobjective optimization, combinatorial optimization, and supervised/unsupervised learning. This emphasis on practical applications will benefit all students, whether they choose to continue their academic career or to enter a particular industry. Introduction to Evolutionary Algorithms is intended as a textbook or self-study material for both advanced undergraduates and graduate students. Additional features such as recommended further reading and ideas for research projects combine to form an accessible and interesting pedagogical approach to this widely used discipline.

Evolutionary Computation

Evolutionary Computation
Author: Kenneth A. De Jong
Publsiher: MIT Press
Total Pages: 256
Release: 2006-02-03
Genre: Computers
ISBN: 9780262041942

Download Evolutionary Computation Book in PDF, Epub and Kindle

This text is an introduction to the field of evolutionary computation. It approaches evolution strategies and genetic programming, as instances of a more general class of evolutionary algorithms.

Creative Evolutionary Systems

Creative Evolutionary Systems
Author: Peter Bentley,David Corne
Publsiher: Morgan Kaufmann
Total Pages: 576
Release: 2002
Genre: Computers
ISBN: 9781558606739

Download Creative Evolutionary Systems Book in PDF, Epub and Kindle

Contenu du disque : Audio CD. Data Track; LadyBug; Olivine Trees; The Rake; Grain Streams (Vanishing Point); Force-4; Living Melodies; Soundscape T2. -- CD-ROM. Origine Generative Form Explorer; The Art of Rendering Music from Cellular Automata; An Evolutionary Environment for Interactive Composition; Visual Aesthetic Evolutionary Design Links; Living Melodies (description and demo software); The Cyclic Glade (artwork); Darwin2K open source toolkit for robot simulation and design; GenePool and Darwin software; Extended version of chapter 5; Soundscape Java Demo; Video of Feeping Creatures