**Getting the book**: http://www.cs.gmu.edu/~sean/book/metaheuristics/

I picked up a copy of this book from the man himself, Sean Luke at the IEEE CEC 2011. I was “aware” of this book from a while back, so I thought it might be a good idea to pick a print copy for light readings during my travels post-conference. Here is a brief review of the book:

**Synopsis:**

As the author states, the book is a compilation of undergraduate lectures notes on Metaheuristics. It focuses on the applications of Metaheuristics to optimization problems including Multi-objective optimization, Combinatorial optimization and Policy optimization. Depending on your experience with Metaheuristics, this book will serve a different purpose for you:

- If you are quite well versed with them, this book will be a nice light reading, with interesting bits and pieces throughout
- If you are starting with them, or want to start with Metaheuristics, this book gives a nice well rounded view of the state-of-the art

**Review**

The book starts with an overview of gradient based optimization methods in Chapter 1 gradually moving to stochastic methods such as randomized hill-climbing, tabu search, simulated annealing in Chapter 2.

Chapter 3 introduces population methods — Evolution Strategies, Genetic Algorithms, Differential Evolution and Particle Swarm Optimization.

Over the last three chapters, the author introduces some fundamental concepts: the choice of representation of solutions, issues of exploration v$ exploitation and local optima traps.

Chapters 4-10 each discuss one specific topic. For example, Chapter 4 is dedicated to representation of solutions — vectors, direct encoded graphs, program trees and rulesets. Chapter 5 discussess parallel methods for metaheuristics and Chapter 7 talks about Multi-objective optimization. Chapter 8 and 10 talks about combinatorial optimization and policy optimization respectively. So, if you are looking for anything specific, you can directly jump to the relevant chapter (assuming, of course that you have the pre-requisite knowledge). As you can see in the ToC, most of the chapters from 4-10 depends on Chapters 3 & 4.

The book finally concludes with some descriptions of test problems and statistical tests that researchers often use to test their algorithms. The very important issue of selecting a proper random number generator is discussed in this chapter.

**Conclusion**

This book along with Evolutionary Computation: A Unified Approach (You may be interested in my review) is great for getting a holistic view of the Meta-heuristic methods, especially if you are more experienced with only one of them.

**Getting the book**: http://www.cs.gmu.edu/~sean/book/metaheuristics/