Differential Evolution

In Search of Solutions

  • Authors
  • Vitaliy Feoktistov

Part of the Springer Optimization and Its Applications book series (SOIA, volume 5)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Pages 41-67
  3. Pages 133-143
  4. Pages 145-148
  5. Back Matter
    Pages 149-196

About this book


The human being aspires to the best possible performance. Both individuals and enterprises are looking for optimal—in other words, the best possible—solutions for situations or problems they face. Most of these problems can be expressed in mathematical terms, and so the methods of optimization undoubtedly render a significant aid.

In cases where there are many local optima; intricate constraints; mixed-type variables; or noisy, time-dependent or otherwise ill-defined functions, the usual methods don’t give satisfactory results. Are you seeking fresh ideas or more efficient methods, or do you perhaps want to be well-informed about the latest achievements in optimization? If so, this book is for you.

This book develops a unified insight on population-based optimization through Differential Evolution, one of the most recent and efficient optimization algorithms. You will find, in this book, everything concerning Differential Evolution and its application in its newest formulation. This book will be a valuable source of information for a very large readership, including researchers, students and practitioners. The text may be used in a variety of optimization courses as well.

Features include:

  • Neoteric view of Differential Evolution
  • Unique formula of global optimization
  • The best known metaheuristics through the prism of Differential Evolution
  • Revolutionary ideas in population-based optimization



Differential Evolution will be of interest to students, teachers, engineers, and researchers from various fields, including computer science, applied mathematics, optimization and operations research, artificial evolution and evolutionary algorithms, telecommunications, engineering design, bioinformatics and computational chemistry, chemical engineering, mechanical engineering, electrical engineering, and physics.


Optimization algorithm Optimization algorithms algorithm algorithms evolution heuristics metaheuristic optimization

Bibliographic information

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