Recent advances in clonal selection algorithms and applications

Luo, Wenjian; Lin, Xin

doi:10.1109/ssci.2017.8285340

Cited by 17 publications

(14 citation statements)

References 92 publications

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“…CSA is an evolutionary algorithm, inspired by the natural phenomenon of the biological immune system, which defends the body against external microorganisms. [ 42 ] reviewed recent works by researchers implementing CSA into their proposed network to deal with constraint optimization tasks, such as pattern recognition [ 43 ], scheduling [ 44 ], fault detection [ 45 ] and dynamic optimization [ 46 ]. Mechanisms of CSA gives the inspiration of specific cells to recognize specific antigens which are later selected to proliferate.…”

Section: Introductionmentioning

confidence: 99%

Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Zamri

Mansor

Kasihmuddin

et al. 2020

Entropy

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Amazon.com Inc. seeks alternative ways to improve manual transactions system of granting employees resources access in the field of data science. The work constructs a modified Artificial Neural Network (ANN) by incorporating a Discrete Hopfield Neural Network (DHNN) and Clonal Selection Algorithm (CSA) with 3-Satisfiability (3-SAT) logic to initiate an Artificial Intelligence (AI) model that executes optimization tasks for industrial data. The selection of 3-SAT logic is vital in data mining to represent entries of Amazon Employees Resources Access (AERA) via information theory. The proposed model employs CSA to improve the learning phase of DHNN by capitalizing features of CSA such as hypermutation and cloning process. This resulting the formation of the proposed model, as an alternative machine learning model to identify factors that should be prioritized in the approval of employees resources applications. Subsequently, reverse analysis method (SATRA) is integrated into our proposed model to extract the relationship of AERA entries based on logical representation. The study will be presented by implementing simulated, benchmark and AERA data sets with multiple performance evaluation metrics. Based on the findings, the proposed model outperformed the other existing methods in AERA data extraction.

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Section: Introductionmentioning

confidence: 99%

Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Zamri

Mansor

Kasihmuddin

et al. 2020

Entropy

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“…FCSA Input: N (the size of the population), n (the number of antibodies selected for cloning), n c (the number of clones), m (the degree of variation), c (Rac1 protein activity threshold) Output: the best antibody (1) Begin (2) Randomly generate N antibodies to form the initial candidate set (3) while not meet algorithm termination conditions do (4) Calculate the affinity Aff ab i of each antibody for antigen in the candidate set and record antibody survival time T ab i (5) Sort the antibodies in the candidate set according to their affinity, and put the best n antibodies into the antibody set Ab s (6) forab i inAb s (7) Update the value of the appropriate memory of antibody ab i : S ab i + � 1. See CLONING METHOD, clone antibody ab i according to n c and Aff ab i , and put all antibodies obtained by cloning into antibody set Ab c (8) end for (9) forab i inAb c (10) See VARIATION METHOD, according to the degree of variation m and the affinity of the antibody for the antigen Aff ab i to mutate ab i (11) if antibody ab i is a variant antibody (12) e ab i survival time T ab i � 0, e appropriate memory intensity S ab i � 1 (13) end if (14) end for (15) Select the N antibodies with the highest antigen affinity in Ab c and Ab to replace the N antibodies in Ab (16) See FORGETTING METHOD, calculate the Rac1 protein activity of each antibody in Ab according to the ratio of T ab i to S ab i (17) if antibody ab i Rac1 protein activity > threshold (18) forget the antibody ab i (19)…”

Section: Resultsmentioning

confidence: 99%

“…e classical clonal selection algorithm has problems of algorithm efficiency, convergence rate, and lack of sufficient theoretical support [12]. erefore, people use the cloning selection algorithm to solve practical problems but also put forward many useful improvements to the algorithm itself.…”

Section: Introductionmentioning

confidence: 99%

“…e clonal selection algorithm sorts the affinity of the antibodies in the set and selects the first n antibodies with high affinity to clone and mutate. Since n depends on manual experience, it is generally set to 10% to 50% of the population size [12]; this selection mechanism inevitably leads to a large number of antibodies with general affinity "retention" in the antibody candidate set. Some were replaced by new antibodies due to low affinity, and the other part did not satisfy the conditions of being replaced and they remained in the antibody candidate set for a long time.…”

Section: Introductionmentioning

confidence: 99%

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Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism

et al. 2020

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The antibody candidate set generated by the clonal selection algorithm has only a small number of antibodies with high antigen affinity to obtain high-frequency mutations. Among other antibodies, some low-affinity antibodies are replaced by new antibodies to participate in the next clonal selection. A large number of antibodies with high affinity make it difficult to participate in clonal selection and exist in antibody concentration for a long time. This part of inactive antibody forms a “black hole” of the antibody set, which is difficult to remove and update in a timely manner, thus affecting the speed at which the algorithm approximates the optimal solution. Inspired by the mechanism of biological forgetting, an improved clonal selection algorithm is proposed to solve this problem. It aims to use the abstract mechanism of biological forgetting to eliminate antibodies that cannot actively participate in high-frequency mutations in the antibody candidate set and to improve the problem of insufficient diversity of antibodies in the clonal selection algorithm, which is prone to fall into the local optimal. Compared with the existing clonal selection and genetic algorithms, the experiment and time complexity analysis show that the algorithm has good optimization efficiency and stability.

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“…In this study, an evolutionary algorithm inspired by the clonal selection theory called CSA is proposed to solve the problem of univariate financial time series prediction. This algorithm is chosen because based on a survey by Luo and Lin [20], CSA has been applied to many optimization problems, including constrained optimization [21], combinatorial optimization [22], and nonlinear optimization [23]. Furthermore, according to Hu, Sun, Nie, Li, and Liu [24], CSA is less complicated than GA because it does not have crossover operation which reduces the computational cost.…”

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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Recent advances in clonal selection algorithms and applications

Cited by 17 publications

References 92 publications

Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism

Univariate Financial Time Series Prediction using Clonal Selection Algorithm

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