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Reliability Investigation of LED Devices for Public Light Applications

Durability, Robustness and Reliability of Photonic Devices Set

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Aerospace Actuators 2

Signal-by-Wire and Power-by-Wire

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Flash Memory Integration

Performance and Energy Considerations

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Mechanics of Aeronautical Solids, Materials and Structures

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Engineering Investment Process

Making Value Creation Repeatable

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Space Strategy

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Distributed Systems

Concurrency and Consistency

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Fatigue of Textile and Short Fiber Reinforced Composites

Durability and Ageing of Organic Composite Materials Set – Volume 1

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Management of the Effects of Coastal Storms

Policy, Scientific and Historical Perspectives

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Computational Color Science

Variational Retinex-like Methods

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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

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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.


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