Application areas of AIS: The past, the present and the future

Hart, Emma; Timmis, Jon

doi:10.1016/j.asoc.2006.12.004

Cited by 298 publications

(107 citation statements)

References 25 publications

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“…Comparing FINERS to FAIRS, although there are no differences in the accuracy for the heterogeneous data, using network feature from the immune system decreases the number of rules in the classifiers. The study solves some limitation shown in (Watkins, 2001;Freitas & Timmis, 2007;Hart & Timmis, 2008;Timmis, 2006). However, FINERS does not show a significant different or improvement on the accuracy and rules reduction on non-heterogeneous data compared to the previous AIS classification models.…”

Section: Resultsmentioning

confidence: 88%

“…Objective Referrences As suggested in (Watkins, 2001;Freitas & Timmis, 2007;Hart & Timmis, 2008;Timmis, 2006), methods of using other types of data need to be explored to allow for greater applicability of this learning paradigm. (Hamaker & Boggess, 2004) has explored variety of similarity measurements in generating classifiers with clonal selection concept or population-based AIS algorithm but a more comprehensive experiment on many problems with heterogeneous types is required in order to prove a high quality classification technique for heterogeneous data types.…”

Section: Conceptmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Immune Network: Classification on Heterogeneous Data

Puteh¹,

Hamdan²,

Omar³

et al. 2010

Machine Learning

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Section: Resultsmentioning

confidence: 88%

Section: Conceptmentioning

confidence: 99%

Artificial Immune Network: Classification on Heterogeneous Data

Puteh¹,

Hamdan²,

Omar³

et al. 2010

Machine Learning

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“…[10,11] Another noticeable algorithm in AIS field is clonal selection algorithm (CLONALG), [12] which on the basis of clonal selection principle, has been widely used in machine learning, pattern recognition, multimodal optimization, etc. [2,5,13] However, compared with evolutionary algorithms (EA), CLONALG is more easily stagnated in the local optimum, because of the quick loss of population adversity when researching landscape is not uniform. [14] Analytically, there are two processes to cause the population adversity loss.…”

Section: Introductionmentioning

confidence: 99%

“…However, from the view of global optimization, RAIN has several critical parameters manually set for efficient initialization, such as the number of *Correspondence to: Feng Qian, State-Key Laboratory of Chemical Engineering, East China University of Science and Technology, Shanghai 200237, China. E-mail: fqian@ecust.edu.cn B cells, the network affinity threshold, and mutation rate, [5] which usually directly influence the complexity and performance of the algorithm, while aiNet has more application dependent parameters to be processed overhead of each iteration than RAIN. [10,11] Another noticeable algorithm in AIS field is clonal selection algorithm (CLONALG), [12] which on the basis of clonal selection principle, has been widely used in machine learning, pattern recognition, multimodal optimization, etc.…”

Section: Introductionmentioning

confidence: 99%

A chaotic immune algorithm with fuzzy adaptive parameters

Qian

2008

Asia-Pacific J Chem Eng

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Adversity loss of the population usually leads to premature convergence problem of the algorithm. To deal with this problem, this paper presents a chaotic hypermutation immune algorithm (CHIA), which is built upon clonal selection principle and immune network regulatory mechanism. In CHIA, a bidirection adaptive chaotic hypermutation is introduced to create antibodies of a new genetic type. Simultaneously, a new immune network regulatory strategy based on the stimulation level of the antibody is devised to maintain current population adversity. Intensive comparison between CHIA and clonal selection algorithm (CLONALG) on function optimization is carried out and the simulation results show that CHIA can overcome the premature convergence problem in CLONALG. Additionally, parameter setting is a crucial and time-consuming task in the algorithm design. Through the parameter analysis of CHIA, two fuzzy modules are constructed to tune hypermutation factor and death ratio automatically, which eventually form an improved fuzzy adaptive chaotic immune algorithm (FACIA). According to the encouraging results obtained, it can be concluded that FACIA can significantly enhance the adaptability and improve the convergent performance of CHIA through the application of fuzzy logic in parameter setting. 

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“…Although immune optimization, a popular research branch, was proved to be potential for dynamic problems because of the inherent diversity and adaptation, more studies on it are concentrated on solving non-constrained dynamic single or multi-objective optimization problems [10][11][12]. It is not clear whether bio-immune inspirations are valuable for handling DCMMO.…”

Section: Introductionmentioning

confidence: 99%

Immune Optimization Approach for Dynamic Constrained Multi-Objective Multimodal Optimization Problems

Zhang¹,

Liao²,

Wang³

2012

AJOR

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This work investigates one immune optimization approach for dynamic constrained multi-objective multimodal optimization in terms of biological immune inspirations and the concept of constraint dominance. Such approach includes mainly three functional modules, environmental detection, population initialization and immune evolution. The first, inspired by the function of immune surveillance, is designed to detect the change of such kind of problem and to decide the type of a new environment; the second generates an initial population for the current environment, relying upon the result of detection; the last evolves two sub-populations along multiple directions and searches those excellent and diverse candidates. Experimental results show that the proposed approach can adaptively track the environmental change and effectively find the global Pareto-optimal front in each environment.

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Application areas of AIS: The past, the present and the future

Cited by 298 publications

References 25 publications

Artificial Immune Network: Classification on Heterogeneous Data

Artificial Immune Network: Classification on Heterogeneous Data

A chaotic immune algorithm with fuzzy adaptive parameters

Immune Optimization Approach for Dynamic Constrained Multi-Objective Multimodal Optimization Problems

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