Genetic Algorithms In Java

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Genetic Algorithms in Java Basics

Author : Lee Jacobson
Publisher : Apress
ISBN 13 : 1484203283
Total Pages : 162 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Genetic Algorithms in Java Basics by : Lee Jacobson

Download or read book Genetic Algorithms in Java Basics written by Lee Jacobson and published by Apress. This book was released on 2015-11-28 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic Algorithms in Java Basics is a brief introduction to solving problems using genetic algorithms, with working projects and solutions written in the Java programming language. This brief book will guide you step-by-step through various implementations of genetic algorithms and some of their common applications, with the aim to give you a practical understanding allowing you to solve your own unique, individual problems. After reading this book you will be comfortable with the language specific issues and concepts involved with genetic algorithms and you'll have everything you need to start building your own. Genetic algorithms are frequently used to solve highly complex real world problems and with this book you too can harness their problem solving capabilities. Understanding how to utilize and implement genetic algorithms is an essential tool in any respected software developers toolkit. So step into this intriguing topic and learn how you too can improve your software with genetic algorithms, and see real Java code at work which you can develop further for your own projects and research. Guides you through the theory behind genetic algorithms Explains how genetic algorithms can be used for software developers trying to solve a range of problems Provides a step-by-step guide to implementing genetic algorithms in Java

Applied Evolutionary Algorithms in Java

Author : Robert Ghanea-Hercock
Publisher : Springer Science & Business Media
ISBN 13 : 0387216154
Total Pages : 232 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Applied Evolutionary Algorithms in Java by : Robert Ghanea-Hercock

Download or read book Applied Evolutionary Algorithms in Java written by Robert Ghanea-Hercock and published by Springer Science & Business Media. This book was released on 2013-03-20 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended for students, researchers, and professionals interested in evolutionary algorithms at graduate and postgraduate level. No mathematics beyond basic algebra and Cartesian graphs methods is required, as the aim is to encourage applying the JAVA toolkit to develop an appreciation of the power of these techniques.

Genetic Algorithms and Machine Learning for Programmers

Author : Frances Buontempo
Publisher : Pragmatic Bookshelf
ISBN 13 : 1680506587
Total Pages : 307 pages
Book Rating : 4.6/5 (85 download)

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Book Synopsis Genetic Algorithms and Machine Learning for Programmers by : Frances Buontempo

Download or read book Genetic Algorithms and Machine Learning for Programmers written by Frances Buontempo and published by Pragmatic Bookshelf. This book was released on 2019-01-23 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems. In this book, you will: Use heuristics and design fitness functions. Build genetic algorithms. Make nature-inspired swarms with ants, bees and particles. Create Monte Carlo simulations. Investigate cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection methods, including tournament and roulette wheels. Learn about heuristics, fitness functions, metrics, and clusters. Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon. What You Need: Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.

Genetic Algorithms and Genetic Programming

Author : Michael Affenzeller
Publisher : CRC Press
ISBN 13 : 1420011324
Total Pages : 395 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Genetic Algorithms and Genetic Programming by : Michael Affenzeller

Download or read book Genetic Algorithms and Genetic Programming written by Michael Affenzeller and published by CRC Press. This book was released on 2009-04-09 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Hands-On Genetic Algorithms with Python

Author : Eyal Wirsansky
Publisher : Packt Publishing Ltd
ISBN 13 : 1838559183
Total Pages : 334 pages
Book Rating : 4.8/5 (385 download)

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Book Synopsis Hands-On Genetic Algorithms with Python by : Eyal Wirsansky

Download or read book Hands-On Genetic Algorithms with Python written by Eyal Wirsansky and published by Packt Publishing Ltd. This book was released on 2020-01-31 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 FeaturesExplore the ins and outs of genetic algorithms with this fast-paced guideImplement tasks such as feature selection, search optimization, and cluster analysis using PythonSolve combinatorial problems, optimize functions, and enhance the performance of artificial intelligence applicationsBook 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 learnUnderstand how to use state-of-the-art Python tools to create genetic algorithm-based applicationsUse genetic algorithms to optimize functions and solve planning and scheduling problemsEnhance the performance of machine learning models and optimize deep learning network architectureApply genetic algorithms to reinforcement learning tasks using OpenAI GymExplore how images can be reconstructed using a set of semi-transparent shapesDiscover other bio-inspired techniques, such as genetic programming and particle swarm optimizationWho 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 Computing

Author : Agoston E. Eiben
Publisher : Springer Science & Business Media
ISBN 13 : 3662050943
Total Pages : 307 pages
Book Rating : 4.6/5 (62 download)

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Book Synopsis Introduction to Evolutionary Computing by : Agoston E. Eiben

Download or read book Introduction to Evolutionary Computing written by Agoston E. Eiben and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration

Author : Earl Cox
Publisher : Academic Press
ISBN 13 : 0121942759
Total Pages : 554 pages
Book Rating : 4.1/5 (219 download)

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Book Synopsis Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration by : Earl Cox

Download or read book Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration written by Earl Cox and published by Academic Press. This book was released on 2005-02 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foundations and ideas -- Principal model types -- Approaches to model building -- Fundamental concepts of fuzzy logic -- Fundamental concepts of fuzzy systems -- Fuzzy SQL and intelligent queries -- Fuzzy clustering -- Fuzzy rule induction -- Fundamental concepts of genetic algorithms -- Genetic resource scheduling optimization -- Genetic tuning of fuzzy models.

Genetic Algorithms in Search, Optimization, and Machine Learning

Author : David Edward Goldberg
Publisher : Addison-Wesley Professional
ISBN 13 :
Total Pages : 436 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Genetic Algorithms in Search, Optimization, and Machine Learning by : David Edward Goldberg

Download or read book Genetic Algorithms in Search, Optimization, and Machine Learning written by David Edward Goldberg and published by Addison-Wesley Professional. This book was released on 1989 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: A gentle introduction to genetic algorithms. Genetic algorithms revisited: mathematical foundations. Computer implementation of a genetic algorithm. Some applications of genetic algorithms. Advanced operators and techniques in genetic search. Introduction to genetics-based machine learning. Applications of genetics-based machine learning. A look back, a glance ahead. A review of combinatorics and elementary probability. Pascal with random number generation for fortran, basic, and cobol programmers. A simple genetic algorithm (SGA) in pascal. A simple classifier system(SCS) in pascal. Partition coefficient transforms for problem-coding analysis.

Programming Challenges

Author : Steven S Skiena
Publisher : Springer Science & Business Media
ISBN 13 : 038722081X
Total Pages : 376 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Programming Challenges by : Steven S Skiena

Download or read book Programming Challenges written by Steven S Skiena and published by Springer Science & Business Media. This book was released on 2006-04-18 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are many distinct pleasures associated with computer programming. Craftsmanship has its quiet rewards, the satisfaction that comes from building a useful object and making it work. Excitement arrives with the flash of insight that cracks a previously intractable problem. The spiritual quest for elegance can turn the hacker into an artist. There are pleasures in parsimony, in squeezing the last drop of performance out of clever algorithms and tight coding. The games, puzzles, and challenges of problems from international programming competitions are a great way to experience these pleasures while improving your algorithmic and coding skills. This book contains over 100 problems that have appeared in previous programming contests, along with discussions of the theory and ideas necessary to attack them. Instant online grading for all of these problems is available from two WWW robot judging sites. Combining this book with a judge gives an exciting new way to challenge and improve your programming skills. This book can be used for self-study, for teaching innovative courses in algorithms and programming, and in training for international competition. The problems in this book have been selected from over 1,000 programming problems at the Universidad de Valladolid online judge. The judge has ruled on well over one million submissions from 27,000 registered users around the world to date. We have taken only the best of the best, the most fun, exciting, and interesting problems available.

Genetic Algorithms in Java

Author : Lee Jacobson
Publisher : Apress
ISBN 13 : 9781484203309
Total Pages : 150 pages
Book Rating : 4.2/5 (33 download)

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Book Synopsis Genetic Algorithms in Java by : Lee Jacobson

Download or read book Genetic Algorithms in Java written by Lee Jacobson and published by Apress. This book was released on 2015-03-07 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic Algorithms in Java is an applied approach to learning and solving problems using genetic algorithms, with working projects and solutions written in the Java programming language. This book will guide you step-by-step through various implementations of Genetic Algorithms and some of their common applications, with the aim to give you a practical understanding allowing you to solve your own unique, individual problems. After reading this book you will be comfortable with the language and concepts involved with Genetic Algorithms and you'll have everything you need to start building your own. Genetic Algorithms are frequently used to solve highly complex real world problems and with this book you too can harness their problem solving capabilities. Understanding how to utilize and implement genetic algorithms is an essential tool in any respected software developers toolkit. So step into this intriguing topic and learn how you too can improve your software with genetic algorithms, and see real Java code at work which you can develop further for your own projects and research. Guides you through the theory behind genetic algorithms Explains how genetic algorithms can be used for software developers trying to solve a range of problems Provides a step-by-step guide to implementing genetic in Java algorithms

Learning Genetic Algorithms with Python

Author : Ivan Gridin
Publisher : BPB Publications
ISBN 13 : 8194837758
Total Pages : 330 pages
Book Rating : 4.1/5 (948 download)

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Book Synopsis Learning Genetic Algorithms with Python by : Ivan Gridin

Download or read book Learning Genetic Algorithms with Python written by Ivan Gridin and published by BPB Publications. This book was released on 2021-02-13 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Artificial Intelligence for Big Data

Author : Anand Deshpande
Publisher : Packt Publishing Ltd
ISBN 13 : 1788476018
Total Pages : 371 pages
Book Rating : 4.7/5 (884 download)

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Book Synopsis Artificial Intelligence for Big Data by : Anand Deshpande

Download or read book Artificial Intelligence for Big Data written by Anand Deshpande and published by Packt Publishing Ltd. This book was released on 2018-05-22 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build next-generation Artificial Intelligence systems with Java Key Features Implement AI techniques to build smart applications using Deeplearning4j Perform big data analytics to derive quality insights using Spark MLlib Create self-learning systems using neural networks, NLP, and reinforcement learning Book Description In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems. What you will learn Manage Artificial Intelligence techniques for big data with Java Build smart systems to analyze data for enhanced customer experience Learn to use Artificial Intelligence frameworks for big data Understand complex problems with algorithms and Neuro-Fuzzy systems Design stratagems to leverage data using Machine Learning process Apply Deep Learning techniques to prepare data for modeling Construct models that learn from data using open source tools Analyze big data problems using scalable Machine Learning algorithms Who this book is for This book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus.

An Introduction to Genetic Algorithms

Author : Melanie Mitchell
Publisher : MIT Press
ISBN 13 : 9780262631853
Total Pages : 226 pages
Book Rating : 4.6/5 (318 download)

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Book Synopsis An Introduction to Genetic Algorithms by : Melanie Mitchell

Download or read book An Introduction to Genetic Algorithms written by Melanie Mitchell and published by MIT Press. This book was released on 1998-03-02 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics—particularly in machine learning, scientific modeling, and artificial life—and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

Introduction to Genetic Algorithms

Author : S.N. Sivanandam
Publisher : Springer Science & Business Media
ISBN 13 : 3540731903
Total Pages : 453 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis Introduction to Genetic Algorithms by : S.N. Sivanandam

Download or read book Introduction to Genetic Algorithms written by S.N. Sivanandam and published by Springer Science & Business Media. This book was released on 2007-10-24 with total page 453 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a basic introduction to genetic algorithms. It provides a detailed explanation of genetic algorithm concepts and examines numerous genetic algorithm optimization problems. In addition, the book presents implementation of optimization problems using C and C++ as well as simulated solutions for genetic algorithm problems using MATLAB 7.0. It also includes application case studies on genetic algorithms in emerging fields.

Clever Algorithms

Author : Jason Brownlee
Publisher : Jason Brownlee
ISBN 13 : 1446785068
Total Pages : 437 pages
Book Rating : 4.4/5 (467 download)

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Book Synopsis Clever Algorithms by : Jason Brownlee

Download or read book Clever Algorithms written by Jason Brownlee and published by Jason Brownlee. This book was released on 2011 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a handbook of algorithmic recipes from the fields of Metaheuristics, Biologically Inspired Computation and Computational Intelligence that have been described in a complete, consistent, and centralized manner. These standardized descriptions were carefully designed to be accessible, usable, and understandable. Most of the algorithms described in this book were originally inspired by biological and natural systems, such as the adaptive capabilities of genetic evolution and the acquired immune system, and the foraging behaviors of birds, bees, ants and bacteria. An encyclopedic algorithm reference, this book is intended for research scientists, engineers, students, and interested amateurs. Each algorithm description provides a working code example in the Ruby Programming Language.

Genetic Programming and Data Structures

Author : W.B. Langdon
Publisher : Springer Science & Business Media
ISBN 13 : 9780792381358
Total Pages : 298 pages
Book Rating : 4.3/5 (813 download)

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Book Synopsis Genetic Programming and Data Structures by : W.B. Langdon

Download or read book Genetic Programming and Data Structures written by W.B. Langdon and published by Springer Science & Business Media. This book was released on 1998-04-30 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computers that `program themselves' has long been an aim of computer scientists. Recently genetic programming (GP) has started to show its promise by automatically evolving programs. Indeed in a small number of problems GP has evolved programs whose performance is similar to or even slightly better than that of programs written by people. The main thrust of GP has been to automatically create functions. While these can be of great use they contain no memory and relatively little work has addressed automatic creation of program code including stored data. This issue is the main focus of Genetic Programming, and Data Structures: Genetic Programming + Data Structures = Automatic Programming!. This book is motivated by the observation from software engineering that data abstraction (e.g., via abstract data types) is essential in programs created by human programmers. This book shows that abstract data types can be similarly beneficial to the automatic production of programs using GP. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! shows how abstract data types (stacks, queues and lists) can be evolved using genetic programming, demonstrates how GP can evolve general programs which solve the nested brackets problem, recognises a Dyck context free language, and implements a simple four function calculator. In these cases, an appropriate data structure is beneficial compared to simple indexed memory. This book also includes a survey of GP, with a critical review of experiments with evolving memory, and reports investigations of real world electrical network maintenance scheduling problems that demonstrate that Genetic Algorithms can find low cost viable solutions to such problems. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! should be of direct interest to computer scientists doing research on genetic programming, genetic algorithms, data structures, and artificial intelligence. In addition, this book will be of interest to practitioners working in all of these areas and to those interested in automatic programming.

A Field Guide to Genetic Programming

Author :
Publisher : Lulu.com
ISBN 13 : 1409200736
Total Pages : 252 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis A Field Guide to Genetic Programming by :

Download or read book A Field Guide to Genetic Programming written by and published by Lulu.com. This book was released on 2008 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic programming (GP) is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until high-fitness solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. This unique overview of this exciting technique is written by three of the most active scientists in GP. See www.gp-field-guide.org.uk for more information on the book.