Committee login






Small thumbnail

Baidu SEO

Challenges and Intricacies of Marketing in China

Small thumbnail

Asymmetric Alliances and Information Systems

Issues and Prospects

Small thumbnail

Technicity vs Scientificity

Complementarities and Rivalries

Small thumbnail

Freshwater Fishes

250 Million Years of Evolutionary History

Small thumbnail

Biostatistics and Computer-based Analysis of Health Data using SAS

Biostatistics and Health Science Set

Small thumbnail

Predictive Control

Small thumbnail

Fundamentals of Advanced Mathematics 1

Categories, Algebraic Structures, Linear and Homological Algebra

Small thumbnail

Swelling Concrete in Dams and Hydraulic Structures

DSC 2017

Small thumbnail

The Chemostat

Mathematical Theory of Microorganims Cultures

Small thumbnail

Earthquake Occurrence

Short- and Long-term Models and their Validation

Small thumbnail

Evolutionary Algorithms

Metaheuristics Set – Volume 9

Alain Pétrowski, Institut Mines-Télécom, Paris-Saclay University, France Sana Ben-Hamida, Paris Ouest University, Paris Dauphine University, France

ISBN: 9781848218048

Publication Date: April 2017   Hardback   256 pp.

130.00 USD

Add to cart


Ebook Ebook


Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods.
In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms.
Chapter 1 describes a generic evolutionary algorithm as well as the basic operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are described and compared on a set of test functions. Chapter 3 considers continuous optimization problems with constraints. Various approaches suitable for evolutionary methods are presented. Chapter 4 is related to combinatorial optimization. It provides a catalog of variation operators to deal with order-based problems. Chapter 5 introduces the basic notions required to understand the issue of multi-objective optimization and a variety of approaches for its application. Finally, Chapter 6 describes different approaches of genetic programming able to evolve computer programs in the context of machine learning.


1. Evolutionary Algorithms.
2. Continuous Optimization.
3. Constrained Continuous Evolutionary Optimization.
4. Combinatorial Optimization.
5. Multi-objective Optimization.
6. Genetic Programming for Machine Learning.

About the Authors

Alain Pétrowski is Associate Professor in the Department of Networks and Mobile Multimedia Services at the Telecom-SudParis, Institut Mines-Télécom, Paris-Saclay University, France. His main research interests are related to optimization, metaheuristics and machine learning.
Sana Ben-Hamida is Associate Professor at the Paris Ouest University and Associate Researcher at the Computer Science Laboratory of the Paris Dauphine University in France. Her main research interests include evolutionary computation, machine learning and related applications.


DownloadTable of Contents - PDF File - 88 Kb

Related Titles

0.21033 s.