Immune co-evolutionary algorithm based partition balancing optimization for tobacco distribution system

Hu, Zhi‐Hua; Ding, Yongsheng; Shao, Qing

doi:10.1016/j.eswa.2008.06.074

Cited by 34 publications

(19 citation statements)

References 29 publications

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“…Artificial immune system is a computational intelligence paradigm inspired by the biological immune system, and has also been applied successfully to a variety of optimization problems [9]. In our paper, a self-adaptive immune algorithm is developed for optimize the parameter of LogitBoost classifier.…”

Section: Self-adaptive Immune Algorithmmentioning

confidence: 99%

Improved LogitBoost Classifier Based Prediction of GPCR-G-Protein Coupling with Self-Adaptive Immune Algorithm

Ding

2010

2010 4th International Conference on Bioinformatics and Biomedical Engineering

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G-Protein coupled receptors (GPCRs) constitute the largest group of membrane receptors with great pharmacological interest. The signal transduction within cells is leaded by a wide range of native ligands interact and activate GPCRs. Most of these responses are mediated through the interaction of GPCRs with coupling GTP-binding proteins (G-proteins).For the reason of the information explosion in biological sequence databases, the development of software algorithms that could predict properties of GPCRs is important. In this paper, we have developed an intensive exploratory approach to predict the coupling preference of GPCRs to heterotrimeric G-proteins. An integrated recognition method combined with Self-Adaptive Immune Algorithm and LogitBoost classifier has been applied in prediction. The result indicates that the proposed method might become a potentially useful tool for GPCR-G-protein coupling prediction, or play a complimentary role to the existing methods in the relevant areas. The method predicts the coupling preferences of GPCRs to three kinds of G-protein subclasses, G s , G i/o and G q/11 , but not G 12/13 for the limited amount.

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Section: Self-adaptive Immune Algorithmmentioning

confidence: 99%

Improved LogitBoost Classifier Based Prediction of GPCR-G-Protein Coupling with Self-Adaptive Immune Algorithm

Ding

2010

2010 4th International Conference on Bioinformatics and Biomedical Engineering

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“…The historical cooperation records are important. (13) RESPONSE The initial network of logistics for the specific demand will basically cover the involved nodes of the task. Therefore, at least two subsets should be clarified: nodes and connections.…”

Section: Invasion Invd Tw Scalementioning

confidence: 99%

“…In [9] , immune principles are employed to design co-evolutionary algorithm with interactive procedure to optimize and evolve the library of design solutions for garment computer-aided system. In [13] , another complex algorithm is proposed to search the districting solutions for large-scale distribution. In [10] , a multi-affinity is proposed to model the complex structural relationship among different element set in multi-objective optimization.…”

Section: Bio-nspired Approachesmentioning

confidence: 99%

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A multi-affinity model for logistics network inspired by bio-system

Hu¹,

Sheng²

2014

IJCIS

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How can we deal with the increasing scale of large logistics network for the booming economy? The structure, mechanisms and principles upon the logistics network show an evolutionary tendency which is complex, and interesting for designing and optimizing logistics network. The adaptability, stability and evolution capability are attractive for artificial intelligent systems. Inspired by biological systems, the metaphors for the structure and eight primary mechanisms are extracted to design a multi-affinity model for bio-inspired logistics network. Then, by mapping the biological elements and functions to logistics network, the evolutionary structure and simulation approach for logistics network are proposed and investigated by experiments. It is a novel way to study the complexity of logistics network and it is an interesting bionic approach for artificial network model. The research result can be employed to study the emergency features and collective behavior of large-scale logistics network.

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“…It introduces the interactive immune co-evolution mechanisms [11][12][13][14][15], and proposes an interactive immune coevolution algorithm (IICEA) model. Also, an expert evaluation model is established for the differential fiber spinning process with IICEA model.…”

Section: Introductionmentioning

confidence: 99%

An interactive immune co-evolutionary design approach for the spinning of differential fibers

Ren

Ding

et al. 2011

2011 6th IEEE Conference on Industrial Electronics and Applications

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Co-evolutionary algorithm behaves in a complicated and counterintuitive way for some complex problems. The introduction of interactive immune concept can increase its convergence rate and effectiveness. This paper represents an optimization process of polyester filament in melt spinning with the interactive immune coevolutionary design approach and also the expert evaluation with entropy-based fuzzy comprehensive evaluation. The simulation results demonstrate its effectiveness. This study also has important value and widespread application prospect regarding the spinning process optimization and the subsequent processing in fiber production lines.

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Immune co-evolutionary algorithm based partition balancing optimization for tobacco distribution system

Cited by 34 publications

References 29 publications

Improved LogitBoost Classifier Based Prediction of GPCR-G-Protein Coupling with Self-Adaptive Immune Algorithm

Improved LogitBoost Classifier Based Prediction of GPCR-G-Protein Coupling with Self-Adaptive Immune Algorithm

A multi-affinity model for logistics network inspired by bio-system

An interactive immune co-evolutionary design approach for the spinning of differential fibers

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