Book Image

Mastering Concurrency Programming with Java 9 - Second Edition

By : Javier Fernández González
Book Image

Mastering Concurrency Programming with Java 9 - Second Edition

By: Javier Fernández González

Overview of this book

Concurrency programming allows several large tasks to be divided into smaller sub-tasks, which are further processed as individual tasks that run in parallel. Java 9 includes a comprehensive API with lots of ready-to-use components for easily implementing powerful concurrency applications, but with high flexibility so you can adapt these components to your needs. The book starts with a full description of the design principles of concurrent applications and explains how to parallelize a sequential algorithm. You will then be introduced to Threads and Runnables, which are an integral part of Java 9's concurrency API. You will see how to use all the components of the Java concurrency API, from the basics to the most advanced techniques, and will implement them in powerful real-world concurrency applications. The book ends with a detailed description of the tools and techniques you can use to test a concurrent Java application, along with a brief insight into other concurrency mechanisms in JVM.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

The second example - a genetic algorithm

Genetic algorithms are adaptive heuristic search algorithms based on the natural selection principles used to generate good solutions to optimization and search problems. They work with possible solutions to a problem, named individuals, or phenotypes. Each individual has a representation formed of a set of properties named chromosomes. Normally, the individuals are represented by a sequence of bits, but you can choose the representation that better fits your problem.

You also need a function to determine whether a solution is good or bad, named the fitness function. The main objective of the genetic algorithm is to find a solution that maximizes or minimizes that function.

The genetic algorithm starts with a set of possible solutions to the problem. This set of possible solutions is called the population. You can generate this initial set randomly or use some kind of heuristic function to obtain better initial solutions.

Once you have the initial population...