Open Access
Application areas of AIS: the past, present and future.
Emma Hart,Jon Timmis +1 more
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TLDR
In this paper, the authors take a step back and reflect on the contributions that the Artificial Immune Systems (AIS) has brought to the application areas to which it has been applied, and suggest a set of problem features that they believe will allow the true potential of the immunological system to be exploited in computational systems.Abstract:
After a decade of research into the area of artificial immune systems, it is worthwhile to take a step back and reflect on the contributions that the
paradigm has brought to the application areas to which it has been applied. Undeniably, there have been a lot of successful stories—however, if the
field is to advance in the future and really carve out its own distinctive niche, then it is necessary to be able to illustrate that there are clear benefits to
be obtained by applying this paradigm rather than others. This paper attempts to take stock of the application areas that have been tackled in the
past, and ask the difficult question ‘‘was it worth it ?’’. We then attempt to suggest a set of problem features that we believe will allow the true
potential of the immunological system to be exploited in computational systems, and define a unique niche for AISread more
Citations
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Clonal selection as an inspiration for adaptive and distributed information processing
TL;DR: This investigation was motivated by three open problems in the broader field of Artificial Immune Systems, specifically the perceived impasse in the development, identity, and application of the field, the promise of distributed information processing, and the need for a framework to motivate such work.
Journal ArticleDOI
An Improved Independent Component Analysis Algorithm Based on Artificial Immune System
Li-Yuan Chen,Chi-Jie Lu +1 more
TL;DR: An improved ICA algorithm based on artificial immune system (AIS) (called AIS-ICA) is presented, to use AIS to determine the separating matrix of ICA.
Journal ArticleDOI
A biological model to improve PE malware detection: Review
TL;DR: The use of the human immune system and co-stimulation signals are explained as a way to build a biological model for improving the ability of PE malware detection systems.
Journal ArticleDOI
A Constructive Data Classification Version of the Particle Swarm Optimization Algorithm
TL;DR: Two new particle swarm algorithms specifically designed to solve classification problems, one of which uses ideas from the immune system to automatically build the swarm and the other is a derivation of a particle swarm clustering algorithm.
DissertationDOI
Model-based calibration of automated transmissions
TL;DR: This dissertation aims to provide a history of web exceptionalism from 1989 to 2002, a period chosen in order to explore its roots as well as specific cases up to and including the year in which descriptions of “Web 2.0” began to circulate.
References
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TL;DR: The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data by carrying out sequential optimization over pairs of input patterns and providing a theoretical analysis of the statistical performance of the algorithm.
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Evolutionary programming made faster
Xin Yao,Yong Liu,Guangming Lin +2 more
TL;DR: A "fast EP" (FEP) is proposed which uses a Cauchy instead of Gaussian mutation as the primary search operator and is proposed and tested empirically, showing that IFEP performs better than or as well as the better of FEP and CEP for most benchmark problems tested.
Journal ArticleDOI
OR-Library: Distributing Test Problems by Electronic Mail
TL;DR: A system (OR-Library) that distributes test problems by electronic mail (e-mail) that has available test problems drawn from a number of different areas of operational research.
Proceedings ArticleDOI
Self-nonself discrimination in a computer
TL;DR: A method for change detection which is based on the generation of T cells in the immune system is described, which reveals computational costs of the system and preliminary experiments illustrate how the method might be applied to the problem of computer viruses.