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Rajwar K, Deep K, Das S. An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges. Artif Intell Rev 2023; 56:1-71. [PMID: 37362893 PMCID: PMC10103682 DOI: 10.1007/s10462-023-10470-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called 'novel' if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on 'novel ideas', so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.
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Affiliation(s)
- Kanchan Rajwar
- Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667 India
| | - Kusum Deep
- Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667 India
| | - Swagatam Das
- Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal 700108 India
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Yu H, Shi J, Qian J, Wang S, Li S. Single dendritic neural classification with an effective spherical search-based whale learning algorithm. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:7594-7632. [PMID: 37161164 DOI: 10.3934/mbe.2023328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
McCulloch-Pitts neuron-based neural networks have been the mainstream deep learning methods, achieving breakthrough in various real-world applications. However, McCulloch-Pitts neuron is also under longtime criticism of being overly simplistic. To alleviate this issue, the dendritic neuron model (DNM), which employs non-linear information processing capabilities of dendrites, has been widely used for prediction and classification tasks. In this study, we innovatively propose a hybrid approach to co-evolve DNM in contrast to back propagation (BP) techniques, which are sensitive to initial circumstances and readily fall into local minima. The whale optimization algorithm is improved by spherical search learning to perform co-evolution through dynamic hybridizing. Eleven classification datasets were selected from the well-known UCI Machine Learning Repository. Its efficiency in our model was verified by statistical analysis of convergence speed and Wilcoxon sign-rank tests, with receiver operating characteristic curves and the calculation of area under the curve. In terms of classification accuracy, the proposed co-evolution method beats 10 existing cutting-edge non-BP methods and BP, suggesting that well-learned DNMs are computationally significantly more potent than conventional McCulloch-Pitts types and can be employed as the building blocks for the next-generation deep learning methods.
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Affiliation(s)
- Hang Yu
- College of Computer Science and Technology, Taizhou University, Taizhou 225300, China
| | - Jiarui Shi
- Department of Engineering, Wesoft Company Ltd., Kawasaki-shi 210-0024, Japan
| | - Jin Qian
- College of Computer Science and Technology, Taizhou University, Taizhou 225300, China
| | - Shi Wang
- College of Computer Science and Technology, Taizhou University, Taizhou 225300, China
| | - Sheng Li
- College of Computer Science and Technology, Taizhou University, Taizhou 225300, China
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Widhalm D, Goeschka KM, Kastner W. A Review on Immune-Inspired Node Fault Detection in Wireless Sensor Networks with a Focus on the Danger Theory. SENSORS (BASEL, SWITZERLAND) 2023; 23:1166. [PMID: 36772205 PMCID: PMC9920811 DOI: 10.3390/s23031166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/09/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
The use of fault detection and tolerance measures in wireless sensor networks is inevitable to ensure the reliability of the data sources. In this context, immune-inspired concepts offer suitable characteristics for developing lightweight fault detection systems, and previous works have shown promising results. In this article, we provide a literature review of immune-inspired fault detection approaches in sensor networks proposed in the last two decades. We discuss the unique properties of the human immune system and how the found approaches exploit them. With the information from the literature review extended with the findings of our previous works, we discuss the limitations of current approaches and consequent future research directions. We have found that immune-inspired techniques are well suited for lightweight fault detection, but there are still open questions concerning the effective and efficient use of those in sensor networks.
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Affiliation(s)
- Dominik Widhalm
- Department Electronic Engineering, University of Applied Sciences Technikum Wien, 1200 Vienna, Austria
| | - Karl M. Goeschka
- Department Electronic Engineering, University of Applied Sciences Technikum Wien, 1200 Vienna, Austria
| | - Wolfgang Kastner
- Automation Systems Group, Faculty of Informatics, TU Wien, 1040 Vienna, Austria
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Ming Z, Liang Y, Zhou W. NDAMM: a numerical differentiation-based artificial macrophage model for anomaly detection. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04334-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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Wang D, Liang Y, Dong H, Tan C, Xiao Z, Liu S. Innate immune memory and its application to artificial immune systems. THE JOURNAL OF SUPERCOMPUTING 2022; 78:11680-11701. [PMID: 35194317 PMCID: PMC8852961 DOI: 10.1007/s11227-021-04295-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 12/29/2021] [Indexed: 06/14/2023]
Abstract
The study of innate immune-based algorithms is an important research domain in Artificial Immune System (AIS), such as Dendritic Cell Algorithm (DCA), Toll-Like Receptor algorithm (TLRA). The parameters in these algorithms usually require either manually pre-defined usually provided by the immunologists, or empirically derived from the training dataset, and result in poor self-adaptation and self-learning. The fundamental reason is that the original innate immune mechanisms lack adaptive biological theory. To solve this problem, a theory called ‘Trained Immunity™ or Innate Immune Memory (IIM)™ that thinks innate immunity can also build immunological memory to enhance the immune system™s learning and adaptive reactions to the second stimulus is introduced into AIS to improve the innate immune algorithms™ adaptability. In this study, we present an overview of IIM with particular emphasis on analogies in the AIS world, and a modified DCA with an effective automated tuning mechanism based on IIM (IIM-DCA) to optimize migration threshold of DCA. The migration threshold of Dendritic Cells (DCs) determines the lifespan of the antigen collected by DCs, and directly affect the detection speed and accuracy of DCA. Experiments on real datasets show that our proposed IIM-DCA which integrates Innate Immune Memory mechanism delivers more accurate results.
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Affiliation(s)
- Dongmei Wang
- School of Computer Science, Wuhan University, Wuhan, 430072 China
| | - Yiwen Liang
- School of Computer Science, Wuhan University, Wuhan, 430072 China
| | - Hongbin Dong
- School of Cyber Science and Engineering, Wuhan University, Wuhan, 430072 China
| | - Chengyu Tan
- School of Computer Science, Wuhan University, Wuhan, 430072 China
| | - Zhenhua Xiao
- School of Computer Science, Wuhan University, Wuhan, 430072 China
| | - Sai Liu
- Collage of Computer Science, South-Central University for Nationalities, Wuhan, 430072 China
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Limon-Cantu D, Alarcon-Aquino V. Multiresolution dendritic cell algorithm for network anomaly detection. PeerJ Comput Sci 2021; 7:e749. [PMID: 34805504 PMCID: PMC8576553 DOI: 10.7717/peerj-cs.749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 09/27/2021] [Indexed: 06/13/2023]
Abstract
Anomaly detection in computer networks is a complex task that requires the distinction of normality and anomaly. Network attack detection in information systems is a constant challenge in computer security research, as information systems provide essential services for enterprises and individuals. The consequences of these attacks could be the access, disclosure, or modification of information, as well as denial of computer services and resources. Intrusion Detection Systems (IDS) are developed as solutions to detect anomalous behavior, such as denial of service, and backdoors. The proposed model was inspired by the behavior of dendritic cells and their interactions with the human immune system, known as Dendritic Cell Algorithm (DCA), and combines the use of Multiresolution Analysis (MRA) Maximal Overlap Discrete Wavelet Transform (MODWT), as well as the segmented deterministic DCA approach (S-dDCA). The proposed approach is a binary classifier that aims to analyze a time-frequency representation of time-series data obtained from high-level network features, in order to classify data as normal or anomalous. The MODWT was used to extract the approximations of two input signal categories at different levels of decomposition, and are used as processing elements for the multi resolution DCA. The model was evaluated using the NSL-KDD, UNSW-NB15, CIC-IDS2017 and CSE-CIC-IDS2018 datasets, containing contemporary network traffic and attacks. The proposed MRA S-dDCA model achieved an accuracy of 97.37%, 99.97%, 99.56%, and 99.75% for the tested datasets, respectively. Comparisons with the DCA and state-of-the-art approaches for network anomaly detection are presented. The proposed approach was able to surpass state-of-the-art approaches with UNSW-NB15 and CSECIC-IDS2018 datasets, whereas the results obtained with the NSL-KDD and CIC-IDS2017 datasets are competitive with machine learning approaches.
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Ezugwu AE, Shukla AK, Nath R, Akinyelu AA, Agushaka JO, Chiroma H, Muhuri PK. Metaheuristics: a comprehensive overview and classification along with bibliometric analysis. Artif Intell Rev 2021. [DOI: 10.1007/s10462-020-09952-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Berquedich M, Berquedich A, Kamach O, Masmoudi M, Chebbak A, Deshayes L. Developing a Mobile COVID-19 Prototype Management Application Integrated With an Electronic Health Record for Effective Management in Hospitals. IEEE ENGINEERING MANAGEMENT REVIEW 2020; 48:55-64. [PMCID: PMC8768976 DOI: 10.1109/emr.2020.3032943] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 10/12/2020] [Indexed: 12/02/2023]
Abstract
This article aimed to develop a mobile application for the management of coronavirus disease 2019 (COVID-19). We analyzed the pilot version with satisfaction through a survey and an interview with health workers from Sidi Said, a local hospital located in Meknès, Morocco, which receives patients suffering from COVID-19. Methods: We have formed a team effort that involves health professionals, specialists from various fields and who participated in the design process of this project. It is a review of the existing literature and interviews with health professionals seeking to trace all the functions of this application. The user interface graphics, whether at the hospital or patient application level, were reviewed for effective usability by a multidisciplinary team. After having had to develop the pilot version of the application, the usefulness and the gratitude of the application were evaluated by eight patients and carers by means of a utility test, based on a real scenario, a utility survey. The objective is to ensure communication between the mobile application and the decision support application at the emergency services level to facilitate the detection of people who had developed COVID-19 as well as follow-up at home for detected patients. Results: The COVID-19 mobile management application provides capture functions and can afford information that helps in the prescription of appropriate drugs to patients at home. It is used to identify people who have been in contact with people who have tested positive for COVID-19, to carry out a large-scale screening by sending alert messages to these people, and to follow up via geolocation. There is also the possibility via Bluetooth, for the monitoring of patients without the activation of the Geolocation option. The approach adopted aims to reduce congestion in hospitals by identifying people suffering from respiratory illnesses or who may be contaminated, and monitoring them remotely. We offered six functions to achieve the objective of this project. Main: Respiratory crisis journal, medication recall, appointment, survey of people contacted, personal equipment and dashboard, daily traceability and monitoring, and geolocation. We have integrated the application of the electronic healthcare registration system in hospitals. To simplify usability, the frequently used functions, which are relatively important, can be found on the main page under the heading “COVID-19 monitoring” (in French: COVID-19 monitoring), and “Medication log” (in French: Journal medication). In addition, during graphic design, art therapy was used to improve the psychological stability of the patient. Eight participants were employed for the evaluation. For scenario-based tasks, out of ten tasks, all participants can record the entries in detail. The system use scale score was 94 points, indicating that the system was satisfactory for the patients and the staff who tested and manipulated it. Conclusion: This article confirmed that patients were satisfied with the follow-up at home and with the caregivers of the hospital. In addition, this made it possible to accurately record their symptoms and, therefore, facilitated an early detection of COVID-19. This was very useful for analyzing crisis trends, responding to a broader detection, avoiding contamination within the same family and provide effective support for COVID-19 positive patients at home. Through integration with the electronic health record, patient health care information can be used by health care decision-makers to manage treatment plans and support medical interventions. Currently, all patients who have been followed remotely are cured. We intend to generalize this concept for the monitoring of other diseases than COVID-19 later.
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Affiliation(s)
- Mouna Berquedich
- University Mohammed VI Polytechnique BenguerirBen Guerir43150Morocco
| | - Amine Berquedich
- University Mohammed VI Polytechnique BenguerirBen Guerir43150Morocco
| | - Oulaid Kamach
- University Mohammed VI Polytechnique BenguerirBen Guerir43150Morocco
| | - Malek Masmoudi
- University Mohammed VI Polytechnique BenguerirBen Guerir43150Morocco
| | - Ahmed Chebbak
- University Mohammed VI Polytechnique BenguerirBen Guerir43150Morocco
| | - Laurent Deshayes
- University Mohammed VI Polytechnique BenguerirBen Guerir43150Morocco
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Molina D, Poyatos J, Ser JD, García S, Hussain A, Herrera F. Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations. Cognit Comput 2020. [DOI: 10.1007/s12559-020-09730-8] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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11
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Juneja K. MPMFFT based DCA-DBT integrated probabilistic model for face expression classification. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2020. [DOI: 10.1016/j.jksuci.2017.10.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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12
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An extensive review of computational intelligence-based optimization algorithms: trends and applications. Soft comput 2020. [DOI: 10.1007/s00500-020-04958-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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13
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Nature Inspired Metaheuristics and Their Applications in Agriculture: A Short Review. PROGRESS IN ARTIFICIAL INTELLIGENCE 2019. [DOI: 10.1007/978-3-030-30241-2_15] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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14
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Survey on evolutionary computation methods for cybersecurity of mobile ad hoc networks. EVOLUTIONARY INTELLIGENCE 2018. [DOI: 10.1007/s12065-018-0154-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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15
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Dendritic Cell Algorithm Applied to Ping Scan Investigation Revisited: Detection Quality and Performance Analysis. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2017. [DOI: 10.1109/tetci.2017.2721449] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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16
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Lasisi A, Ghazali R, Deris MM, Herawan T, Lasisi F. Extracting Information in Agricultural Data Using Fuzzy-Rough Sets Hybridization and Clonal Selection Theory Inspired Algorithms. INT J PATTERN RECOGN 2016. [DOI: 10.1142/s0218001416600089] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Mining agricultural data with artificial immune system (AIS) algorithms, particularly the clonal selection algorithm (CLONALG) and artificial immune recognition system (AIRS), form the bedrock of this paper. The fuzzy-rough feature selection (FRFS) and vaguely quantified rough set (VQRS) feature selection are coupled with CLONALG and AIRS for improved detection and computational efficiencies. Comparative simulations with sequential minimal optimization and multi-layer perceptron reveal that the CLONALG and AIRS produced significant results. Their respective FRFS and VQRS upgrades namely, FRFS-CLONALG, FRFS-AIRS, VQRS-CLONALG, and VQRS-AIRS, are able to generate the highest detection rates and lowest false alarm rates. Thus, gathering useful information with the AIS models can help to enhance productivity related to agriculture.
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Affiliation(s)
- Ayodele Lasisi
- Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
| | - Rozaida Ghazali
- Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
| | - Mustafa Mat Deris
- Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
| | - Tutut Herawan
- Faculty of Computer Science and Information Technology, University of Malaya 50603 Kuala Lumpur, Malaysia
| | - Fola Lasisi
- Department of Agricultural Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
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An upper and lower CUSUM for signal normalization in the dendritic cell algorithm. EVOLUTIONARY INTELLIGENCE 2016. [DOI: 10.1007/s12065-016-0136-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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A survey of artificial immune system based intrusion detection. ScientificWorldJournal 2014; 2014:156790. [PMID: 24790549 PMCID: PMC3981469 DOI: 10.1155/2014/156790] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2013] [Accepted: 12/30/2013] [Indexed: 11/17/2022] Open
Abstract
In the area of computer security, Intrusion Detection (ID) is a mechanism that attempts to discover abnormal access to computers by analyzing various interactions. There is a lot of literature about ID, but this study only surveys the approaches based on Artificial Immune System (AIS). The use of AIS in ID is an appealing concept in current techniques. This paper summarizes AIS based ID methods from a new view point; moreover, a framework is proposed for the design of AIS based ID Systems (IDSs). This framework is analyzed and discussed based on three core aspects: antibody/antigen encoding, generation algorithm, and evolution mode. Then we collate the commonly used algorithms, their implementation characteristics, and the development of IDSs into this framework. Finally, some of the future challenges in this area are also highlighted.
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Elsayed SAM, Rajasekaran S, Ammar RA. Integrating Clonal Selection and Deterministic Sampling for Efficient Associative Classification. PROCEEDINGS OF THE ... CONGRESS ON EVOLUTIONARY COMPUTATION. CONGRESS ON EVOLUTIONARY COMPUTATION 2014:3236-3243. [PMID: 24500504 DOI: 10.1109/cec.2013.6557966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Traditional Associative Classification (AC) algorithms typically search for all possible association rules to find a representative subset of those rules. Since the search space of such rules may grow exponentially as the support threshold decreases, the rules discovery process can be computationally expensive. One effective way to tackle this problem is to directly find a set of high-stakes association rules that potentially builds a highly accurate classifier. This paper introduces AC-CS, an AC algorithm that integrates the clonal selection of the immune system along with deterministic data sampling. Upon picking a representative sample of the original data, it proceeds in an evolutionary fashion to populate only rules that are likely to yield good classification accuracy. Empirical results on several real datasets show that the approach generates dramatically less rules than traditional AC algorithms. In addition, the proposed approach is significantly more efficient than traditional AC algorithms while achieving a competitive accuracy.
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Affiliation(s)
- Samir A Mohamed Elsayed
- Computer Science Department, University of Connecticut, Storrs, CT 06269, Helwan University, Cairo, Egypt
| | | | - Reda A Ammar
- Computer Science Department, University of Connecticut, Storrs, CT 06269
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22
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Algorithms in nature: the convergence of systems biology and computational thinking. Mol Syst Biol 2011; 7:546. [PMID: 22068329 PMCID: PMC3261700 DOI: 10.1038/msb.2011.78] [Citation(s) in RCA: 73] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2011] [Accepted: 09/07/2011] [Indexed: 01/30/2023] Open
Abstract
Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high-level design principles of biological systems. This Perspectives discusses the recent convergence of these two ways of thinking. Computer science and biology have enjoyed a long and fruitful relationship for decades. Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high-level design principles of biological systems. Recently, these two directions have been converging. In this review, we argue that thinking computationally about biological processes may lead to more accurate models, which in turn can be used to improve the design of algorithms. We discuss the similar mechanisms and requirements shared by computational and biological processes and then present several recent studies that apply this joint analysis strategy to problems related to coordination, network analysis, and tracking and vision. We also discuss additional biological processes that can be studied in a similar manner and link them to potential computational problems. With the rapid accumulation of data detailing the inner workings of biological systems, we expect this direction of coupling biological and computational studies to greatly expand in the future.
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Zhang C, Yi Z. A danger theory inspired artificial immune algorithm for on-line supervised two-class classification problem. Neurocomputing 2010. [DOI: 10.1016/j.neucom.2010.01.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Guzella T, Mota-Santos T, Uchôa J, Caminhas W. Identification of SPAM messages using an approach inspired on the immune system. Biosystems 2008; 92:215-25. [DOI: 10.1016/j.biosystems.2008.02.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2007] [Revised: 02/23/2008] [Accepted: 02/23/2008] [Indexed: 11/28/2022]
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Improving the reliability of real-time embedded systems using innate immune techniques. EVOLUTIONARY INTELLIGENCE 2008. [DOI: 10.1007/s12065-008-0009-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Oates R, Kendall G, Garibaldi JM. Frequency analysis for dendritic cell population tuning. EVOLUTIONARY INTELLIGENCE 2008. [DOI: 10.1007/s12065-008-0011-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Guzella TS, Mota-Santos TA, Caminhas WM. A Novel Immune Inspired Approach to Fault Detection. LECTURE NOTES IN COMPUTER SCIENCE 2007. [DOI: 10.1007/978-3-540-73922-7_10] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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34
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Greensmith J, Aickelin U, Twycross J. Articulation and Clarification of the Dendritic Cell Algorithm. LECTURE NOTES IN COMPUTER SCIENCE 2006. [DOI: 10.1007/11823940_31] [Citation(s) in RCA: 67] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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35
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Danger Is Ubiquitous: Detecting Malicious Activities in Sensor Networks Using the Dendritic Cell Algorithm. LECTURE NOTES IN COMPUTER SCIENCE 2006. [DOI: 10.1007/11823940_30] [Citation(s) in RCA: 33] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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Kim J, Wilson WO, Aickelin U, McLeod J. Cooperative Automated Worm Response and Detection ImmuNe ALgorithm(CARDINAL) Inspired by T-Cell Immunity and Tolerance. ACTA ACUST UNITED AC 2005. [DOI: 10.1007/11536444_13] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
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