Diversity and artificial immune systems: Incorporating a diversity operator into aiNet

P S Andrews; J Timmis

Diversity and artificial immune systems: Incorporating a diversity operator into aiNet

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

When constructing biologically inspired algorithms, important properties to consider are openness, diversity, interaction, structure and scale. In this paper, we focus on the property of diversity. Introducing diversity into biologically inspired paradigms is a key feature of their success. Within the field of Artificial Immune Systems, little attention has been paid to this issue. Typically, techniques of diversity introduction, such as simple random number generation, are employed with little or no consideration to the application area. Using function optimisation as a case study, we propose a simple immune inspired mutation operator that is tailored to the problem at hand. We incorporate this diversity operator into a well known immune inspired algorithm, aiNet. Through this approach, we show that it is possible to improve the search capability of aiNet on hard to locate optima. We further illustrate that by incorporating the same mutation operator into aiNet when applied to clustering, it is observed that performance is neither improved nor sacrificed.

Original language	English
Title of host publication	NEURAL NETS
Editors	B Apolloni, M Marinaro, G Necosia, R Tagliaferri
Place of Publication	BERLIN
Publisher	Springer
Pages	293-306
Number of pages	14
ISBN (Print)	3-540-33183-2
Publication status	Published - 2006
Event	16th Italian Workshop on Neural Nets - Vietri sul Mare Duration: 8 Jun 2005 → 11 Jun 2005

Conference

Conference	16th Italian Workshop on Neural Nets
City	Vietri sul Mare
Period	8/06/05 → 11/06/05

Cite this

@inproceedings{9ce44eb7ddfc4598b2f20a55f8acb119,

title = "Diversity and artificial immune systems: Incorporating a diversity operator into aiNet",

abstract = "When constructing biologically inspired algorithms, important properties to consider are openness, diversity, interaction, structure and scale. In this paper, we focus on the property of diversity. Introducing diversity into biologically inspired paradigms is a key feature of their success. Within the field of Artificial Immune Systems, little attention has been paid to this issue. Typically, techniques of diversity introduction, such as simple random number generation, are employed with little or no consideration to the application area. Using function optimisation as a case study, we propose a simple immune inspired mutation operator that is tailored to the problem at hand. We incorporate this diversity operator into a well known immune inspired algorithm, aiNet. Through this approach, we show that it is possible to improve the search capability of aiNet on hard to locate optima. We further illustrate that by incorporating the same mutation operator into aiNet when applied to clustering, it is observed that performance is neither improved nor sacrificed.",

author = "Andrews, {P S} and J Timmis",

year = "2006",

language = "English",

isbn = "3-540-33183-2",

pages = "293--306",

editor = "B Apolloni and M Marinaro and G Necosia and R Tagliaferri",

booktitle = "NEURAL NETS",

publisher = "Springer",

address = "Germany",

note = "16th Italian Workshop on Neural Nets ; Conference date: 08-06-2005 Through 11-06-2005",

}

TY - GEN

T1 - Diversity and artificial immune systems: Incorporating a diversity operator into aiNet

AU - Andrews, P S

AU - Timmis, J

PY - 2006

Y1 - 2006

N2 - When constructing biologically inspired algorithms, important properties to consider are openness, diversity, interaction, structure and scale. In this paper, we focus on the property of diversity. Introducing diversity into biologically inspired paradigms is a key feature of their success. Within the field of Artificial Immune Systems, little attention has been paid to this issue. Typically, techniques of diversity introduction, such as simple random number generation, are employed with little or no consideration to the application area. Using function optimisation as a case study, we propose a simple immune inspired mutation operator that is tailored to the problem at hand. We incorporate this diversity operator into a well known immune inspired algorithm, aiNet. Through this approach, we show that it is possible to improve the search capability of aiNet on hard to locate optima. We further illustrate that by incorporating the same mutation operator into aiNet when applied to clustering, it is observed that performance is neither improved nor sacrificed.

AB - When constructing biologically inspired algorithms, important properties to consider are openness, diversity, interaction, structure and scale. In this paper, we focus on the property of diversity. Introducing diversity into biologically inspired paradigms is a key feature of their success. Within the field of Artificial Immune Systems, little attention has been paid to this issue. Typically, techniques of diversity introduction, such as simple random number generation, are employed with little or no consideration to the application area. Using function optimisation as a case study, we propose a simple immune inspired mutation operator that is tailored to the problem at hand. We incorporate this diversity operator into a well known immune inspired algorithm, aiNet. Through this approach, we show that it is possible to improve the search capability of aiNet on hard to locate optima. We further illustrate that by incorporating the same mutation operator into aiNet when applied to clustering, it is observed that performance is neither improved nor sacrificed.

M3 - Conference contribution

SN - 3-540-33183-2

SP - 293

EP - 306

BT - NEURAL NETS

A2 - Apolloni, B

A2 - Marinaro, M

A2 - Necosia, G

A2 - Tagliaferri, R

PB - Springer

CY - BERLIN

T2 - 16th Italian Workshop on Neural Nets

Y2 - 8 June 2005 through 11 June 2005

ER -