| Description |
1 online resource : illustrations |
| Series |
Expert's voice in Java |
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Expert's voice in Java.
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| Contents |
At a Glance; Contents; About the Authors; About the Technical Reviewers; Preface; Chapter 1: Introduction; What is Artificial Intelligence?; Biologically Analogies; History of Evolutionary Computation; The Advantage of Evolutionary Computation; Biological Evolution; An Example of Biological Evolution; Basic Terminology; Terms; Search Spaces; Fitness Landscapes; Local Optimums; Parameters; Mutation Rate; Population Size; Crossover Rate; Genetic Representations; Termination; The Search Process; CITATIONS; Chapter 2: Implementation of a Basic Genetic Algorithm |
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Pre-Implementation Pseudo Code for a Basic Genetic Algorithm; About the Code Examples in this Book; Basic Implementation; The Problem ; Parameters ; Initialization ; Evaluation ; Termination Check ; Crossover ; Roulette Wheel Selection; Crossover Methods; Crossover Pseudo Code; Crossover Implementation; Elitism ; Mutation ; Execution ; Summary ; Chapter 3: Robotic Controllers; Introduction; The Problem; Implementation; Before You Start; Encoding; Initialization; Evaluation; Termination Check; Selection Method and Crossover; Tournament Selection; Single Point Crossover |
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Execution Summary; Exercises; Chapter 4: Traveling Salesman; Introduction; The Problem; Implementation; Before You Start; Encoding; Initialization; Evaluation; Termination Check; Crossover; Mutation; Execution; Summary; Exercises; Chapter 5: Class Scheduling; Introduction; The Problem; Implementation; Before You Start; Encoding; Initialization; The Executive Class; Evaluation; Termination; Mutation; Execution; Analysis and Refinement; Exercises; Summary; Chapter 6: Optimization; Adaptive Genetic Algorithms; Implementation; Exercises; Multi-Heuristics |
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Implementation Exercises; Performance Improvements; Fitness Function Design; Parallel Processing; Fitness Value Hashing; Encoding; Mutation and Crossover Methods; Summary; Index |
| Summary |
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 |
| Notes |
Includes index |
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Online resource; title from PDF title page (EBSCO, viewed December 10, 2015) |
| In |
Springer eBooks |
| Subject |
Java (Computer program language)
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Computer algorithms.
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Algorithms
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algorithms.
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Artificial intelligence.
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Programming & scripting languages: general.
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COMPUTERS -- Programming Languages -- Java.
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Computer algorithms
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Java (Computer program language)
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| Form |
Electronic book
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| Author |
Kanber, Burak, author
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| ISBN |
9781484203286 |
|
1484203283 |
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