Chasing chaos

Kelsey, J.; Timmis, Jon; Hone, Andrew N. W.

doi:10.1109/cec.2003.1299605

Cited by 13 publications

(10 citation statements)

References 9 publications

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“…Predictions are judged by their training label compared to the AIS prediction and result in T cell participation counters for true positives c T P , true negatives c T N , false positives c F P and false negatives c F N . We emphasize that only T cells with a fitting receptor participate in these decisions (see equation 8). Furthermore, the counts are incremented with respect to the T cell prediction.…”

Section: Evaluation and Selection Of T Cellsmentioning

confidence: 99%

“…memorization of classified patterns, self organization for adaptivity and co-stimulation to control immune responses. Immune algorithms are inspired by these mechanisms to perform anomaly detection [7], optimization [8], network surveillance [9], [10], [11], [12], [13] and data analysis [14]. Even protein structure prediction [15] and robot control [16] was performed using AIS.…”

Section: Introductionmentioning

confidence: 99%

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An artificial t cell immune system for predicting MHC-II binding peptides

Henneges¹,

Huster²,

Zell³

2009

2009 IEEE Symposium on Artificial Life

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One key principle of natural immune systems is the extracellular presentation of peptides bound to MHC-II complexes on the cell surface to represent the internal state. The prediction of those peptides that are presented became a current research topic in machine learning, as they may be used as potential vaccines for immunization. In addition the biological immune system (IS) is a learning system in its own right. In this work, we design an artificial immune system (AIS) that is based on observations of the natural immune system to predict MHC-II binding peptides. Our strategy simulates the mutable receptors of T lymphocytes as well as their selection during life time. We model the receptor specificity and binding mode as well as the lymphocyte's influence during an inflammatory response. Finally, our implementation uses the pathogen specificity of T cells to model the prediction problem.

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Section: Evaluation and Selection Of T Cellsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An artificial t cell immune system for predicting MHC-II binding peptides

Henneges¹,

Huster²,

Zell³

2009

2009 IEEE Symposium on Artificial Life

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“…These include simple models of clonal selection and immune networks [30][31][32][33][34][35], and negative selection algorithms [36,37]. For the model based on clonal selection theory, some typical algorithms such as CLONal ALGorithm (CLONALG) [14], the B cell algorithm (BCA) [38][39][40], and multi-objective immune system algorithm (MISA) [41] are based purely on mutation and selection mechanisms for randomly generated clones (clonal selection); AIS algorithms that are inspired by the network of interactions in the adaptive immune system include the resource-limited artificial immune network (RAIN) [42], artificial immune network (AINE) [43] and artificial immune NETwork(aiNET) [44].…”

Section: Previous Workmentioning

confidence: 99%

An immune memory clonal algorithm for numerical and combinatorial optimization

Liu

Jiao

et al. 2010

Front. Comput. Sci. China

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Inspired by the clonal selection theory together with the immune network model, we present a new artificial immune algorithm named the immune memory clonal algorithm (IMCA). The clonal operator, inspired by the immune system, is discussed first. The IMCA includes two versions based on different immune memory mechanisms; they are the adaptive immune memory clonal algorithm (AIMCA) and the immune memory clonal strategy (IMCS). In the AIMCA, the mutation rate and memory unit size of each antibody is adjusted dynamically. The IMCS realizes the evolution of both the antibody population and the memory unit at the same time. By using the clonal selection operator, global searching is effectively combined with local searching. According to the antibody-antibody (Ab-Ab) affinity and the antibody-antigen (Ab-Ag) affinity, The IMCA can adaptively allocate the scale of the memory units and the antibody population. In the experiments, 18 multimodal functions ranging in dimensionality from two, to one thousand and combinatorial optimization problems such as the traveling salesman and knapsack problems (KPs) are used to validate the performance of the IMCA. The computational cost per iteration is presented. Experimental results show that the IMCA has a high convergence speed and a strong ability in enhancing the diversity of the population and avoiding premature convergence to some degree. Theoretical roof is provided that the IMCA is convergent with probability 1.

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“…Again, each of these has its own bias, and the affinity function must be selected with great care, as it can affect the overall performance (and ultimately the result) of the system [Preitas k, Timmis 2003]. This was also recently shown experimentally in the case of immune networks, where the affinity function affected the overall outcome of the shape of the network [Hart k. Ross 2004 The final layer involves the use of algorithms, which govern the behavior (dynamics) of the system.…”

Section: Application Domainmentioning

confidence: 99%

In Silico Immunology

Flower¹,

Timmis²

2007

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Chasing chaos

Cited by 13 publications

References 9 publications

An artificial t cell immune system for predicting MHC-II binding peptides

An artificial t cell immune system for predicting MHC-II binding peptides

An immune memory clonal algorithm for numerical and combinatorial optimization

In Silico Immunology

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