By Thomas Weise
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. With this book, two major audience groups are addressed:
Topics
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. With this book, two major audience groups are addressed:
- It can help students since we try to describe the algorithms in an understandable, consistent way and, maybe even more important, includes much of the background knowledge needed to understand them. Thus, you can find summaries on stochastic theory and theoretical computer science in Part IV on page 455. Additionally, application examples are provided which give an idea how problems can be tackled with the different techniques and what results can be expected.
- Fellow researchers and PhD students may find the application examples helpful too. For them, in-depth discussions on the single methodologies are included that are supported with a large set of useful literature references.
Topics
- Evolutionary Algorithms
- Genetic Algorithms
- Genetic Programming
- Learning Classifier Systems
- Hill Climbing
- Simulated Annealing
- Example Applications
- Sigoa – Implementation in Java
- Background (Mathematics, Computer Science)