Evolutionary Computation: 1st Edition (Hardback) book cover

Evolutionary Computation

1st Edition

By D. Dumitrescu, Beatrice Lazzerini, Lakhmi C. Jain, A. Dumitrescu

CRC Press

424 pages

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Hardback: 9780849305887
pub: 2000-06-22
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pub: 2000-06-22
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Rapid advances in evolutionary computation have opened up a world of applications-a world rapidly growing and evolving. Decision making, neural networks, pattern recognition, complex optimization/search tasks, scheduling, control, automated programming, and cellular automata applications all rely on evolutionary computation.

Evolutionary Computation presents the basic principles of evolutionary computing: genetic algorithms, evolution strategies, evolutionary programming, genetic programming, learning classifier systems, population models, and applications. It includes detailed coverage of binary and real encoding, including selection, crossover, and mutation, and discusses the (m+l) and (m,l) evolution strategy principles. The focus then shifts to applications: decision strategy selection, training and design of neural networks, several approaches to pattern recognition, cellular automata, applications of genetic programming, and more.


"…offers a presentation of the main ideas, models, and algorithms of evolutionary computation. Anyone who is inspired by the work of L. Davis or D.E. Goldberg to use evolutionary computation as a tool in hos/her daily work will find a complete overview of techniques and strategies…Anyone interested in optimization or search problems can find useful ideas, methods, and algorithms."

- Journal of Chemometrics, 2002

Table of Contents

Principles of Evolutionary Computation

Genes and chromosomes

Early EC research

Basic evolutionary computation models

Other EC approaches

Structure of an evolutionary algorithm

Basic evolutionary algorithm

Genetic Algorithms

Problem representation and fitness function

Search progress

Basic elements of genetic algorithms

Canonical genetic algorithm

Schemata and building blocks

Basic Selection Schemes in Evolutionary Algorithms

Selection purposes

Fitness function

Selection pressure and takeover time

Proportional selection


Selection Based on Scaling and Ranking Mechanisms

Scale transformation

Static scaling mechanisms

Dynamic scaling

Noisy fitness functions

Fitness remapping for minimization problems

Rank-based selection

Binary tournament

q-tournament selection

Further Selection Strategies

Classification of selection strategies

Elitist strategies

Generation gap methods

Steady-state evolutionary algorithms

Generational elitist strategies in GAs

Michalewicz selection

Boltzmann selection

Other selection methods

Genetic drift

Recombination Operators within Binary Encoding

One-point crossover

Two-point crossover

N-point crossover

Punctuated crossover

Segmented crossover

Shuffle crossover

Uniform crossover

Other crossover operators and some comparisons

Crossover probability


N-point crossover algorithm

Selection for survival or replacement

General remarks about crossover within the framework of binary encoding

Mutation and other Search Operators

Mutation with binary encoding

Strong and weak mutation operators

Non-uniform mutation

Adaptive non-uniform mutation

Self-adaptation of mutation rate

Crossover versus mutation

Inversion operator

Selection versus variation operators

Simple genetic algorithm revisited

Schema Theorem, Building Blocks and Related Topics

Elements characterizing schemata

Schema dynamics

Effect of selection on schema dynamics

Effect of recombination on schema dynamics

Combined effect of selection and recombination on schema dynamics

Effect of mutation on schema dynamics

Schema theorem

Building block

Building block hypothesis and linkage problem

Generalizations of schema theorem

Deceptive functions

Real-Valued Encoding

Real-valued vectors

Recombination operators for real-valued encoding

Mutation operators for real-valued encoding

Hybridization, Parameter Setting and Adaptation

Specialized representation and hybridization within GAs

Parameter setting and adaptive GAs

Adaptive GAs

Adaptive Representations: Messy Genetic Algorithms, Delta Coding and Diploidic Representation

Principles of messy genetic algorithms

Recombination within messy genetic operators


Computational model and results on messy GAs

Generalizations of messy GAs

Other adaptive representation approaches

Delta coding

Diploidy and dominance

Evolution Strategies and Evolutionary Programming

Evolution strategies

(1+1) strategy

Multimembered evolution strategies

Standard mutation

Genotypes including covariance matrix. Correlated mutation

Cauchy perturbations

Evolutionary programming

Evolutionary programming using Cauchy perturbation

Population Models and Parallel Implementations

Niching methods

Fitness sharing


Island and stepping stone models

Fine-grained and diffusion models


Baldwin effect

Parallel implementation of evolutionary algorithms

Genetic Programming

Early GP approaches

Program generating language

GP program structures

Initialization of tree structures

Fitness calculation

Recombination operators



Population models

Parallel implementation

Basic GP algorithm

Learning Classifier Systems

Michigan and Pittsburg families of learning classifier systems

Michigan classifier systems

Bucket brigade algorithm

Pittsburgh classifier systems

Fuzzy classifier systems

Applications of Evolutionary Computation

General applications of evolutionary computation

Main application areas

Optimization and search applications

Choosing a decision strategy

Neural network training and design

Pattern recognition applications

Cellular automata

Evolutionary algorithms versus other heuristics

About the Series

International Series on Computational Intelligence

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Software Development & Engineering / Systems Analysis & Design
COMPUTERS / Computer Engineering