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Genetic Algorithms in Java Basics 1st ed. [Mīkstie vāki]

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  • Formāts: Paperback / softback, 154 pages, height x width: 235x155 mm, weight: 2701 g, 33 Illustrations, black and white; XV, 154 p. 33 illus., 1 Paperback / softback
  • Izdošanas datums: 27-Nov-2015
  • Izdevniecība: APress
  • ISBN-10: 1484203291
  • ISBN-13: 9781484203293
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Genetic Algorithms in Java Basics 1st ed.Palielināt
  • Formāts: Paperback / softback, 154 pages, height x width: 235x155 mm, weight: 2701 g, 33 Illustrations, black and white; XV, 154 p. 33 illus., 1 Paperback / softback
  • Izdošanas datums: 27-Nov-2015
  • Izdevniecība: APress
  • ISBN-10: 1484203291
  • ISBN-13: 9781484203293
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781484203293.html
Keywords:
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
About the Authors x
About the Technical Reviewers xi
Preface xiii
Chapter 1 Introduction
1(20)
What is Artificial Intelligence?
1(1)
Biologically Analogies
2(1)
History of Evolutionary Computation
3(1)
The Advantage of Evolutionary Computation
4(2)
Biological Evolution
6(2)
An Example of Biological Evolution
7(1)
Basic Terminology
8(1)
Terms
8(1)
Search Spaces
9(6)
Fitness Landscapes
9(3)
Local Optimums
12(3)
Parameters
15(1)
Mutation Rate
15(1)
Population Size
16(1)
Crossover Rate
16(1)
Genetic Representations
16(1)
Termination
17(1)
The Search Process
17(2)
Citations
19(2)
Chapter 2 Implementation of a Basic Genetic Algorithm
21(26)
Pre-Implementation
21(1)
Pseudo Code for a Basic Genetic Algorithm
22(1)
About the Code Examples in this Book
22(1)
Basic Implementation
23(21)
The Problem
23(1)
Parameters
24(1)
Initialization
25(5)
Evaluation
30(2)
Termination Check
32(2)
Crossover
34(6)
Elitism
40(1)
Mutation
41(2)
Execution
43(1)
Summary
44(3)
Chapter 3 Robotic Controllers
47(34)
Introduction
47(1)
The Problem
48(1)
Implementation
49(29)
Before You Start
49(1)
Encoding
50(3)
Initialization
53(6)
Evaluation
59(9)
Termination Check
68(3)
Selection Method and Crossover
71(6)
Execution
77(1)
Summary
78(3)
Exercises
79(2)
Chapter 4 Traveling Salesman
81(24)
Introduction
81(2)
The Problem
83(1)
Implementation
83(19)
Before You Start
83(1)
Encoding
84(1)
Initialization
84(3)
Evaluation
87(4)
Termination Check
91(1)
Crossover
92(4)
Mutation
96(2)
Execution
98(4)
Summary
102(3)
Exercises
103(2)
Chapter 5 Class Scheduling
105(34)
Introduction
105(1)
The Problem
106(1)
Implementation
107(28)
Before You Start
107(600)
Encoding
707
Initialization
108(13)
The Executive Class
121(606)
Evaluation
727(1)
Termination
728
Mutation
130(2)
Execution
132(3)
Analysis and Refinement
135(2)
Exercises
137(1)
Summary
137(2)
Chapter 6 Optimization
139(14)
Adaptive Genetic Algorithms
139(4)
Implementation
140(2)
Exercises
142(1)
Multi-Heuristics
143(1)
Implementation
143(1)
Exercises
144(1)
Performance Improvements
144(6)
Fitness Function Design
145(1)
Parallel Processing
145(1)
Fitness Value Hashing
146(3)
Encoding
149(1)
Mutation and Crossover Methods
149(1)
Summary
150(3)
Index 153
Lee Jacobson is a professional freelance software developer from Bristol, England who first began writing code at the age of 15 while trying to write his own games. His interest soon transitioned to software development and computer science which led him to the field of artificial intelligence. He found a passion for the subject after studying Genetic Algorithms and other optimization techniques at university. He would often enjoy spending his evenings learning about optimization algorithms such as genetic algorithms and how he could use them to solve various problems.