Genetic Algorithms in Java Basics

This book is the Chinese translation of the English original Genetic Algorithms in Java Basics, published by Posts & Telecom Press in December 2016. It runs to about 199 pages across six chapters. It introduces genetic algorithms—an evolutionary computation method—in as simple and direct a way as possible, and provides ready-to-run Java code implementations for the example problems discussed in the book. It is a typical hands-on, learn-by-doing title. Chapter 1 starts from artificial intelligence, introduces the biological-evolution analogy, the history of evolutionary computation, its advantages and limitations, and sets out basic concepts and terms such as fitness landscapes, local optima, mutation rate, population size, crossover rate, gene representation, and termination conditions. Chapter 2 builds a basic genetic algorithm from scratch, covering parameter setup, population initialization, fitness evaluation, termination checks, roulette-wheel selection, crossover methods, elitism, and mutation, with exercises at the end. Chapters 3 to 5 analyze and code around a robot controller, the traveling salesman problem, and a timetabling problem respectively, grounding abstract algorithmic principles in concrete scenarios. Chapter 6 discusses parameter tuning and implementation-level optimization techniques for genetic algorithms. The book is code-driven with theory as a supporting thread; it is concise and suits readers who want to learn genetic algorithms as a tool in their toolbox rather than as a research subject. Note that the content is positioned as a basic introduction and does not cover advanced topics such as genetic programming or multi-objective optimization, and the scale of the examples is relatively limited.