From the preface:

This e-book is devoted to global optimization algorithms, which are methods
to find optimal solutions for given problems. It especially focuses on evolution-
ary computation by discussing evolutionary algorithms, genetic algorithms,
genetic programming, learning classifier systems, evolution strategy, differen-
tial evolution, particle swarm optimization, and ant colony optimization. It
also elaborates on meta-heuristics like simulated annealing, hill climbing, tabu
search, and random optimization.

With this book, we want to address two major audience groups:
1. It can help students since we try to describe the algorithms in an un-
derstandable, consistent way and, maybe even more important, include
all background knowledge needed to understand them. Thus, you can
find summaries on stochastic theory and theoretical computer science
in Part IV on page 471. Additionally, application examples are provided
which give an idea how problems can be tackled with the different tech-
niques and what results can be expected.

2. Fellow researchers and PhD students maybe will find the application ex-
amples helpful too. For them, in-depth discussions on the single method-
ologies are included that are supported with a large set of useful literature
references.