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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

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1. Introduction to Genetic Algorithms

2. Implementing A Genetic Algorithm in Java

3. Evolving Robot Controls with GA

4. The Traveling Salesman Problem

5. Scheduling Problems with GA

6. Optimization Strategies with GA

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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.

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