Evolutionary Algorithms

Book Description

Evolutionary are bio-inspired based on Darwin’s 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 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 . 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 able to evolve programs in the context of machine learning.

Table of Contents

Chapter 1 Evolutionary Algorithms
Chapter 2 Continuous Optimization
Chapter 3 Constrained Continuous Evolutionary Optimization
Chapter 4 Combinatorial Optimization
Chapter 5 Multi-Objective Optimization
Chapter 6 Genetic Programming For Machine Learning

Book Details

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ZippyShare Click to downloadAZW3704/13/2017
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