Recent Advances in Artificial Immune Systems: Models and Applications

Dasgupta, Dipankar; Yu, Senhua; Niño, Fernando

doi:10.1016/j.asoc.2010.08.024

Cited by 358 publications

(171 citation statements)

References 107 publications

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“…To address such problems, bioinspired intelligence algorithms (Da Silva Santos et al, 2010;Gao, 2012) have attracted more and more interest, among which the immunological algorithm (IA) is a particular class of optimization methods inspired by the basic features of adaptive immune response to antigenic stimulus. Most IAs mimic the metaphors of clonal selection principle (de Castro and Zuben, 2002), hypermutation (Freitas and Timmis, 2007), receptor editing (Gao et al, 2007) and lateral interaction effect (Whitbrook et al, 2007), providing a promising search mechanism by exploiting and exploring the solution space in parallel and effectively (Dasgupta et al, 2011). The main unique property of IAs is the utilization of the clonal proliferation, and the clonal selection which returns promising solutions acquired in the learning process.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the learning capacity of immunological algorithms: a comprehensive study of learning operators

Gao¹,

Gong²,

Zhong³

et al. 2013

Advances in Artificial Life, ECAL 2013

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Immunological algorithms are a kind of bio-inspired intelligence methods which draw inspiration from natural immune systems. The problem-solving performance of immunological algorithms mainly lies on the utilization of learning (i.e. mutation) operators. In this paper, nine different learning operators in a standard immune algorithmic framework are investigated. These learning operators consist of eight existing operators and a newly proposed search direction based operator. Experiments are conducted based on nine variants of immunological algorithms that use different learning operators. Simulation results on a large number of benchmark optimization problems give a deep insight into the characteristics of these operators, and further verify that the proposed new learning operator can greatly improve the performance of immunological algorithms.

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Section: Introductionmentioning

confidence: 99%

Enhancing the learning capacity of immunological algorithms: a comprehensive study of learning operators

Gao¹,

Gong²,

Zhong³

et al. 2013

Advances in Artificial Life, ECAL 2013

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“…Artificial immune algorithms are a special class of biologically inspired algorithms, which are based on the biological immune system of vertebrates and derive from various immunological theories, namely the clonal selection principle, negative selection, immune networks or the danger theory [4], [5]. Besides the natural tasks of anomaly detection and classification, they are often applied to function optimization.…”

Section: Learning Operators In Aismentioning

confidence: 99%

A Multi-Learning Immune Algorithm for Numerical Optimization

Wang

Gao

Aorigele

et al. 2015

IEICE Trans. Fundamentals

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SUMMARYThe emergence of nature-inspired algorithms (NIA) is a great milestone in the field of computational intelligence community. As one of the NIAs, the artificial immune algorithm (AIS) mimics the principles of the biological immune system, and has exhibited its effectiveness, implicit parallelism, flexibility and applicability when solving various engineering problems. Nevertheless, AIS still suffers from the issues of evolution premature, local minima trapping and slow convergence due to its inherent stochastic search dynamics. Much effort has been made to improve the search performance of AIS from different aspects, such as population diversity maintenance, adaptive parameter control, etc. In this paper, we propose a novel multi-learning operator into the AIS to further enrich the search dynamics of the algorithm. A framework of embedding multiple commonly used mutation operators into the antibody evolution procedure is also established. Four distinct learning operators including baldwinian learning, cauchy mutation, gaussian mutation and lateral mutation are selected to merge together as a multi-learning operator. It can be expected that the multi-learning operator can effectively balance the exploration and exploitation of the search by enriched dynamics. To verify its performance, the proposed algorithm, which is called multi-learning immune algorithm (MLIA), is applied on a number of benchmark functions. Experimental results demonstrate the superiority of the proposed algorithm in terms of convergence speed and solution quality.

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“…To date, there have been four types of AIS algorithms used in applied AIS [13]: negative selection algorithms, clonal selection algorithms, immune network algorithms and dendritic cell algorithms. Immune-based learning mainly involves clonal selection algorithms and immune network algorithms.…”

Section: Immune-based Learningmentioning

confidence: 99%