You are here

Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications

Publisher: 
Chapman & Hall/CRC
Number of Pages: 
365
Price: 
79.95
ISBN: 
9781584886297
Date Received: 
Monday, September 7, 2009
Reviewable: 
No
Include In BLL Rating: 
No
Reviewer Email Address: 
M. Affenzeller, S. Wagner, S. Winkler, and A. Beham
Publication Date: 
2009
Format: 
Hardcover
Category: 
Monograph

Introduction

Simulating Evolution: Basics about Genetic Algorithms

The Evolution of Evolutionary Computation

The Basics of Genetic Algorithms (GAs)

Biological Terminology

Genetic Operators

Problem Representation

GA Theory: Schemata and Building Blocks

Parallel Genetic Algorithms

The Interplay of Genetic Operators

Bibliographic Remarks

Evolving Programs: Genetic Programming

Introduction: Main Ideas and Historical Background

Chromosome Representation

Basic Steps of the Genetic Programming (GP)-Based Problem Solving Process

Typical Applications of GP

GP Schema Theories

Current GP Challenges and Research Areas

Conclusion

Bibliographic Remarks

Problems and Success Factors

What Makes GAs and GP Unique Among Intelligent Optimization Methods?

Stagnation and Premature Convergence

Preservation of Relevant Building Blocks

What Can Extended Selection Concepts Do to Avoid Premature Convergence?

Offspring Selection (OS)

The Relevant Alleles Preserving Genetic Algorithm (RAPGA)

Consequences Arising out of Offspring Selection and RAPGA

SASEGASA—More Than the Sum of All Parts

The Interplay of Distributed Search and Systematic Recovery of Essential Genetic Information

Migration Revisited

SASEGASA: A Novel and Self-Adaptive Parallel Genetic Algorithm

Interactions between Genetic Drift, Migration, and Self-Adaptive Selection Pressure

Analysis of Population Dynamics

Parent Analysis

Genetic Diversity

Characteristics of Offspring Selection and the RAPGA

Introduction

Building Block Analysis for Standard GAs

Building Block Analysis for GAs Using Offspring Selection

Building Block Analysis for the RAPGA

Combinatorial Optimization: Route Planning

The Traveling Salesman Problem

The Capacitated Vehicle Routing Problem

Evolutionary System Identification

Data-Based Modeling and System Identification

GP-Based System Identification in HeuristicLab

Local Adaption Embedded in Global Optimization

Similarity Measures for Solution Candidates

Applications of Genetic Algorithms: Combinatorial Optimization

The Traveling Salesman Problem

Capacitated Vehicle Routing

Data-Based Modeling with Genetic Programming

Time Series Analysis

Classification

Genetic Propagation

Single Population Diversity Analysis

Multi-Population Diversity Analysis

Code Bloat, Pruning, and Population Diversity

Conclusion and Outlook

Symbols and Abbreviations

References

Index

Publish Book: 
Modify Date: 
Monday, September 7, 2009

Dummy View - NOT TO BE DELETED