Differential Evolution: Fundamentals and Applications

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.