Global Optimization Algorithms, Theory and Application, Weise T., 2009

Global Optimization Algorithms, Theory and Application, Weise T., 2009.

   This e-book is devoted to 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. It also elaborates on other metaheuristics like Simulated Annealing, Extremal Optimization, Tabu Search, and Random Optimization. The book is no book in the conventional sense: Because of frequent updates and changes, it is not really intended for sequential reading but more as some sort of material collection, encyclopedia, or reference work where you can look up stuff, find the correct context, and are provided with fundamentals.




Classification According to Properties.
The taxonomy just introduced classifies the optimization methods according to their algorithmic structure and underlying principles, in other words, from the viewpoint of theory. A software engineer or a user who wants to solve a problem with such an approach is however more interested in its “interfacing features” such as speed and precision.

Speed and precision are conflicting objectives, at least in terms of probabilistic algorithms. A general rule of thumb is that you can gain improvements in accuracy of optimization only by investing more time. Scientists in the area of global optimization try to push this Pareto frontier further by inventing new approaches and enhancing or tweaking existing ones.

Contents.
Preface. 
Contents.
Part I Global Optimization.
1 Introduction.
2 Evolutionary Algorithms.
3 Genetic Algorithms.
4 Genetic Programming.
5 Evolution Strategy.
8 Ant Colony Optimization.
9 Particle Swarm Optimization    
10 Hill Climbing    
11 Random Optimization    
12 Simulated Annealing    
13 Extremal Optimization    
14 Tabu Search    
15 Memetic and Hybrid Algorithms    
16 Downhill Simplex (Nelder and Mead).
17 State Space Search    
18 Parallelization and Distribution.
19 Maintaining the Optimal Set     
Part II Applications.
20 Experimental Settings, Measures, and Evaluations.
21 Benchmarks and Toy Problems.
22 Contests    
23 Real-World Applications    
24 Research Applications.
Part III Sigoa — Implementation in Java.
25 Introduction    
26 Examples.
Part IV Background.
27 Set Theory.
28 Stochastic Theory and Statistics.
29 Clustering.
30 Theoretical Computer Science.
Appendices.
A GNU Free Documentation License (FDL).
B GNU Lesser General Public License (LPGL).
C Credits and Contributors. 
D Citation Suggestion.
References.
Index.
List of Figures.
List of Tables. 
List of Algorithms. 
List of Listings.



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