Clonal selection has been a dominant theme in many immune-inspired algorithms applied to machine learning and optimisation. We examine existing clonal selections algorithms for learning from a theoretical and empirical perspective and assert that the widely accepted computational interpretation of clonal selection is compromised both algorithmically and biologically. We suggest a more capable a...

clustering is the process of dividing a set of input data into a number of subgroups. the members of each subgroup are similar to each other but different from members of other subgroups. the genetic algorithm has enjoyed many applications in clustering data. one of these applications is the clustering of images. the problem with the earlier methods used in clustering images was in selecting in...

This research discusses intrusion detection systems based on computer networks and a model for the detection of malware using artificial immune system (AIS). The SIA has three main theories: the clonal selection, negative selection and network theory. This work used the ClonalG algorithm developed by Castro & Timmis (2002) [5] and implemented in Weka 3.6.4 for the intrusions detection in the KD...

The natural progression from a clonal selection algorithm, is to employ that algorithm in a loose architectural model of the acquired immune system. A series of multiple discrete repertoire models were previously proposed to do just that. These models are briefly reviewed in this work, and elaborated to a multiple repertoire system. This system is defined in the context of a series of principle...

This paper presents a hierarchical clustering algorithm for crop type classification problem using multi-spectral satellite image. In unsupervised techniques, the automatic generation of clusters and its centers is not exploited to their full potential. Hence, a hierarchical clustering algorithm is proposed which uses splitting and merging techniques. Initially, the splitting method is used to ...

We utilize optimization algorithms inspired by the immune system for treating the mixed H2=H1 control problem. Both precisely known systems and uncertain systems with polytopic uncertainties are investigated. For the latter, a novel methodology is proposed to compute the worst case norms within the polytope of matrices. This methodology consists in defining the worst case norm computation as an...

 The clonal selection principle is used to explain the basic features of an adaptive immune response to an antigenic stimulus. It establishes the idea that only those cells that recognize the antigens are selected to proliferate. The selected cells are subject to an affinity maturation process, which improves their affinity to the selective antigens. In this paper, we propose a computational i...

In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search...

The lymphatic system provides the context for the immune system cells and setting for their interactions as described by immunological theory. A previously defined and elaborated lymphoid tissue model is proposed as a general lymphoid-tissue inspired information processing architecture, with specific information processing and information transmission constraints imposed on different tissue typ...

A method based on clonal selection algorithm (CSA) for computing the radius of circular microstrip antennas (MSAs) is presented. The CSA is a stochastic method which has the capability of optimizing multimodal problems. The method gives the radius as output for given values of resonant frequency (fr), height (h), dielectric constant (εr) of the circular MSAs. Results obtained for the radius of ...