A Genetic Algorithm Tutorial

Covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms.

**Tag(s):**
Artificial Intelligence

**Publication date**: 31 Dec 1994

**ISBN-10**:
n/a

**ISBN-13**:
n/a

**Paperback**:
37 pages

**Views**: 40,640

A Genetic Algorithm Tutorial

Covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms.

Tutorial Abstract:

This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm.

The tutorial begins with a very low level discussion of optimization to both introduce basic ideas in optimization as well as basic concepts that relate to genetic algorithms. In section 2 a canonical genetic algorithm is reviewed. In section 3 the principle of hyperplane sampling is explored and some basic crossover operators are introduced. In section 4 various versions of the schema theorem are developed in a step by step fashion and other crossover operators are discussed. In section 5 binary alphabets and their effects on hyperplane sampling are considered. In section 6 a brief criticism of the schema theorem is considered and in section 7 an exact model of the genetic algorithm is developed.

The last three sections of the tutorial cover alternative forms of genetic algorithms and evolutionary computational models, including specialized parallel implementations.

Intended Audience:

The goal of this tutorial is to present genetic algorithms in such a way that students new to this field can grasp the basic concepts behind genetic algorithms as they work through the tutorial. It should allow the more sophisticated reader to absorb this material with relative ease. The tutorial also covers topics, such as inversion, which have sometimes been misunderstood and misused by researchers new to the field.

This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm.

The tutorial begins with a very low level discussion of optimization to both introduce basic ideas in optimization as well as basic concepts that relate to genetic algorithms. In section 2 a canonical genetic algorithm is reviewed. In section 3 the principle of hyperplane sampling is explored and some basic crossover operators are introduced. In section 4 various versions of the schema theorem are developed in a step by step fashion and other crossover operators are discussed. In section 5 binary alphabets and their effects on hyperplane sampling are considered. In section 6 a brief criticism of the schema theorem is considered and in section 7 an exact model of the genetic algorithm is developed.

The last three sections of the tutorial cover alternative forms of genetic algorithms and evolutionary computational models, including specialized parallel implementations.

Intended Audience:

The goal of this tutorial is to present genetic algorithms in such a way that students new to this field can grasp the basic concepts behind genetic algorithms as they work through the tutorial. It should allow the more sophisticated reader to absorb this material with relative ease. The tutorial also covers topics, such as inversion, which have sometimes been misunderstood and misused by researchers new to the field.

Tweet

About The Author(s)

Prof. Darrell Whitley is Chair of the Department of Computer Science at Colorado State University.

Prof. Darrell Whitley is Chair of the Department of Computer Science at Colorado State University.

Book Categories

Computer Science
Introduction to Computer Science
Introduction to Computer Programming
Algorithms and Data Structures
Artificial Intelligence
Computer Vision
Machine Learning
Neural Networks
Game Development and Multimedia
Data Communication and Networks
Coding Theory
Computer Security
Information Security
Cryptography
Information Theory
Computer Organization and Architecture
Operating Systems
Image Processing
Parallel Computing
Concurrent Programming
Relational Database
Document-oriented Database
Data Mining
Big Data
Data Science
Digital Libraries
Compiler Design and Construction
Functional Programming
Logic Programming
Object Oriented Programming
Formal Methods
Software Engineering
Agile Software Development
Information Systems
Geographic Information System (GIS)

Mathematics
Mathematics
Algebra
Abstract Algebra
Linear Algebra
Number Theory
Numerical Methods
Precalculus
Calculus
Differential Equations
Category Theory
Proofs
Discrete Mathematics
Theory of Computation
Graph Theory
Real Analysis
Complex Analysis
Probability
Statistics
Game Theory
Queueing Theory
Operations Research
Computer Aided Mathematics

Supporting Fields
Web Design and Development
Mobile App Design and Development
System Administration
Cloud Computing
Electric Circuits
Embedded System
Signal Processing
Integration and Automation
Network Science
Project Management

Operating System
Programming/Scripting
Ada
Assembly
C / C++
Common Lisp
Forth
Java
JavaScript
Lua
Rexx
Microsoft .NET
Perl
PHP
R
Python
Rebol
Ruby
Scheme
Tcl/Tk

Miscellaneous
Sponsors