University of Science and Technology of China, 2009,-820 pp.
This e-book is devoted to global optimization algorithms, which are methods to find opti- mal solutions for given problems. It especially focuses on Evolutionary Computation by dis- cussing evolutionary algorithms, genetic algorithms, Genetic Programming, Leaing Classi- fier Systems, Evolution Strategy, Differential Evolution, Particle Swarm Optimization, and Ant Colony Optimization. It also elaborates on other metaheuristics like Simulated An- nealing, 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:
1. It can help students since we try to describe the algorithms in an understandable, consis- tent 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 the- oretical 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.
2. 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.
If this book contains something you want to cite or reference in your work, please use the citation suggestion provided in Chapter D on page 591.
Evolutionary Algorithms.
Genetic Algorithms.
Genetic Programming.
Leaing Classifier Systems.
Hill Climbing.
Simulated Annealing.
Example Applications.
Sigoa – Implementation in Java.
Background (Mathematics, Computer Science.
This e-book is devoted to global optimization algorithms, which are methods to find opti- mal solutions for given problems. It especially focuses on Evolutionary Computation by dis- cussing evolutionary algorithms, genetic algorithms, Genetic Programming, Leaing Classi- fier Systems, Evolution Strategy, Differential Evolution, Particle Swarm Optimization, and Ant Colony Optimization. It also elaborates on other metaheuristics like Simulated An- nealing, 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:
1. It can help students since we try to describe the algorithms in an understandable, consis- tent 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 the- oretical 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.
2. 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.
If this book contains something you want to cite or reference in your work, please use the citation suggestion provided in Chapter D on page 591.
Evolutionary Algorithms.
Genetic Algorithms.
Genetic Programming.
Leaing Classifier Systems.
Hill Climbing.
Simulated Annealing.
Example Applications.
Sigoa – Implementation in Java.
Background (Mathematics, Computer Science.