Artificial Immune Systems for Optimization

Bernardino, Heder S.; Barbosa, Hélio J. C.

doi:10.1007/978-3-642-00267-0_14

Cited by 39 publications

(14 citation statements)

References 57 publications

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“…We firstly introduce antigens and antibodies. In the TSP problem, antigens are equivalent to the fitness function defined as equation (1). Antibody generated in the immune response is treated as the feasible solution of the TSP problem.…”

Section: B Parallel Model For Ia In Gpumentioning

confidence: 99%

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A Parallel Immune Algorithm Based on Fine-Grained Model with GPU-Acceleration

Zhang

Liu

2009

2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC)

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Fine-grained parallel immune algorithm (FGIA), though a popular and robust strategy for solving complicated optimization problems, is sometimes inconvenient to use as its population size is restricted by heavy data communication and the parallel computers are relatively difficult to use, manage, maintain and may not be accessible to most researchers. In this paper, we propose a FGIA method based on GPUacceleration, which maps parallel IA algorithm to GPU through the CUDA. We implement the IA on the base of the framework of genetic algorithm (GA), the analytical results demonstrate that the proposed method increases the population size, speeds up its execution and provides ordinary users with a feasible FGIA solution.

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Section: B Parallel Model For Ia In Gpumentioning

confidence: 99%

“…Immune Algorithm (IA) is one of the most powerful methods available for solving hard combinatorial optimization problems [1][2]. In the past few years, IA algorithms have been successfully applied in many different application areas owing to its robustness and simplicity [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A Parallel Immune Algorithm Based on Fine-Grained Model with GPU-Acceleration

Zhang

Liu

2009

2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC)

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“…The proposed job sequence is as follow, 1,7,13,5,6,12,4,10,11,3,9,15,2,8,14 The makespan of existing scheduling is 67.79 hours or more than 2 days and 19 hours to produce the 2400m length of each job. While on the selected proposed scheduling the makespan is 50.42 hours to produce the same length of products per job.…”

Section: VImentioning

confidence: 99%

Flowshop Scheduling With Sequence Dependent Set-Up Time Using Immune Algorithm to Reduce Makespan in Pt. Xyz Bandung as a Textile Industry

Nasution¹,

Damayanti²,

Juliani³

2019

IJIES

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In manufacturing operations, production system is certainly an important aspect. Thus, company should do the planning and production control well in order to have satisfactory system. One of the production planning process is scheduling. This process is one of the main process in production planning. The urge of doing the scheduling process is due to scheduling is the details of the planning process. The production scheduling in textile industry is considered as the industry with the hybrid flow shop scheduling type. Moreover, in this company there are several machine have the characteristic of sequence dependent setup time. Thus, in order to get the optimal solution this research used Immune Algorithm. This algorithm combines an efficient mutation to cooperate the pursuit for a near-optimal solution. Keywords—Hybrid flow shops, Sequence Dependent Setup Times, Makespan, Immune Algorithm

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“…These algorithms were inspired by theories from immunology namely clonal selection theory, negative selection theory, danger theory, and artificial immune network theory. It gained popularity by efficiently solving numerous optimization problems [12] and has been applied in numerous domains such as computer security [13], optimization [14], disease diagnosis [15] and bankruptcy prediction [15]. Most of its achievements were contributed by CSA [16].…”

Section: Introductionmentioning

confidence: 99%

Univariate Financial Time Series Prediction using Clonal Selection Algorithm

Azlan

Yusof

Mohsin

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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The ability to predict the financial market is beneficial not only to the individual but also to the organization and country. It is not only beneficial in terms of financial but also in terms of making a short-term and long-term decision. This paper presents an experimental study to perform univariate financial time series prediction using a clonal selection algorithm (CSA). CSA is an optimization algorithm that is based on clonal selection theory. It is a subset of the artificial immune system, a class of evolutionary algorithms inspired by the immune system of a vertebrate. Since CSA is an optimization algorithm, the univariate financial time series prediction problem was modeled into an optimization problem using a weighted regression model. CSA was used to search for the optimal set of weights for the regression model to generate prediction with the lowest error. Three data sets from the financial market were chosen for the experiments of this study namely S&P500 price, Gold price, and EUR-USD exchange rate. The performance of CSA is measured using RMSE. The value of RMSE for a problem is related to the maximum and minimum value of the data set. Therefore, the results were not compared to other data sets. Instead, it is compared to the range of values of the data sets. The result of the experiments shows that CSA can make decent predictions for financial time series despite being inferior to ARIMA. Hence, this finding implies that CSA can be implemented on a univariate financial time series prediction problem given that the problem is modeled as an optimization problem.

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Artificial Immune Systems for Optimization

Cited by 39 publications

References 57 publications

A Parallel Immune Algorithm Based on Fine-Grained Model with GPU-Acceleration

A Parallel Immune Algorithm Based on Fine-Grained Model with GPU-Acceleration

Flowshop Scheduling With Sequence Dependent Set-Up Time Using Immune Algorithm to Reduce Makespan in Pt. Xyz Bandung as a Textile Industry

Univariate Financial Time Series Prediction using Clonal Selection Algorithm

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