TY - GEN
T1 - On diversity and artificial immune systems
T2 - 16th Italian Workshop on Neural Nets, WIRN 2005, and International Workshop on Natural and Artificial Immune Systems, NAIS 2005
AU - Andrews, Paul S.
AU - Timmis, Jon
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.
UR - http://www.scopus.com/inward/record.url?scp=33745788814&partnerID=8YFLogxK
U2 - 10.1007/11731177_37
DO - 10.1007/11731177_37
M3 - Conference Proceeding (Non-Journal item)
AN - SCOPUS:33745788814
SN - 3540331832
SN - 9783540331834
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 293
EP - 306
BT - Neural Nets - 16th Italian Workshop on Neural Nets, WIRN 2005, and International Workshop on Natural and Artificial Immune Systems, NAIS 2005, Revised Selected Papers
A2 - Apolloni, Bruno
A2 - Marinaro, Maria
A2 - Nicosia, Giuseppe
A2 - Tagliaferri, Roberto
PB - Springer Nature
Y2 - 8 June 2005 through 11 June 2005
ER -