關於這本電子書
In the field of evolutionary computation, differential evolution, also known as DE, is a method that optimizes a problem by iteratively trying to improve a candidate solution with relation to a specific measure of quality. DE is an abbreviation for the term "differential evolution." These kinds of procedures are typically referred to as metaheuristics since they make very few or no assumptions about the issue that is being addressed and are able to search very huge spaces of potential solutions. However, metaheuristics like as DE do not guarantee that the best solution will ever be discovered.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Differential evolution
Chapter 2: Artificial bee colony algorithm
Chapter 3: Evolutionary computation
Chapter 4: Evolution strategy
Chapter 5: CMA-ES
Chapter 6: Genetic algorithm
Chapter 7: Parallel computing
Chapter 8: Multi-objective optimization
Chapter 9: Constrained optimization
Chapter 10: Quasi-Newton method
(II) Answering the public top questions about differential evolution.
(III) Real world examples for the usage of differential evolution in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of differential evolution' technologies.
Who This Book Is For
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of differential evolution.
