This book is about global optimization algorithms, which are methods to find optimal solutions for given problems. It especially focuses on evolutionary computation by discussing evolutionary algorithms, genetic algorithms, genetic programming, learning classifier systems, evolution strategy, differential evolution, particle swarm optimization, and ant colony optimization.

The book also elaborates on other meta-heuristics, such as simulated annealing, hill climbing, tabu search, and random optimization.

According to the author, the book is an ongoing work in progress that has existed for over two years and has been updated and extended throughout that time. However, the book will never be finished since there is always something new to add.

Chapters include:

* Evolutionary Algorithms
* Genetic Algorithms
* Genetic Programming
* Evolution Strategy
* Evolutionary Programming
* Learning Classifier Systems
* Hill Climbing
* Random Optimization
* Simulated Annealing
* Tabu Search
* Ant Colony Optimization
* Particle Swarm Optimization
* Memetic Algorithms
* State Space Search
* Parallelization and Distribution
* Maintaining the Optimal Set
* Benchmarks and Toy Problems
* Contests
* Real-World Applications
* Research Applications
* Sigoa – Implementation in Java
* Set Theory
* Stochastic Theory
* Clustering
* Theoretical Computer Science