IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Genetic Algorithms

Genetic Algorithms
View Sample PDF
Author(s): Darryl Charles (University of Ulster, Ireland), Colin Fyfe (University of Paisley, UK), Daniel Livingstone (University of Paisley, UK)and Stephen McGlinchey (University of Paisley, UK)
Copyright: 2008
Pages: 16
Source title: Biologically Inspired Artificial Intelligence for Computer Games
Source Author(s)/Editor(s): Darryl Charles (University of Ulster, Ireland), Colin Fyfe (University of Paisley, UK), Daniel Livingstone (University of Paisley, UK)and Stephen McGlinchey (University of Paisley, UK)
DOI: 10.4018/978-1-59140-646-4.ch007

Purchase

View Genetic Algorithms on the publisher's website for pricing and purchasing information.

Abstract

The methods in this chapter were developed in response to the need for general purpose methods for solving complex optimisation problems. A classical problem addressed is the Travelling Salesman Problem in which a salesman must visit each of n cities once and only once in an optimum order - that which minimises his travelling. While not typical of a problem encountered in a computer game context, the problem of optimising responses or strategies clearly is applicable.

Related Content

S. Karthigai Selvi, Sharmistha Dey, Siva Shankar Ramasamy, Krishan Veer Singh. © 2025. 16 pages.
S. Sheeba Rani, M. Mohammed Yassen, Srivignesh Sadhasivam, Sharath Kumar Jaganathan. © 2025. 22 pages.
U. Vignesh, K. Gokul Ram, Abdulkareem Sh. Mahdi Al-Obaidi. © 2025. 22 pages.
Monica Bhutani, Monica Gupta, Ayushi Jain, Nishant Rajoriya, Gitika Singh. © 2025. 24 pages.
U. Vignesh, Arpan Singh Parihar. © 2025. 34 pages.
Sharmistha Dey, Krishan Veer Singh. © 2025. 20 pages.
Kalpana Devi. © 2025. 26 pages.
Body Bottom