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• Artificial immune systems-based classification model is proposed. • Artificial immune systems are inspired by biological immune system. • Artificial immune systems are used to optimize the Long short-term memory model. • The artificial immune systems-based LSTM model is used to classify code-mixed data. The main focus of the paper is to propose an artificial immune systems-based classification model for code-mixed social media data. The artificial immune systems are computational models inspired by the biological immune system. In this paper, artificial immune systems are used to optimize the initial parameters of Long short-term memory (LSTM) model. The proposed artificial immune systems-based LSTM model is then used for the classification of code-mixed data. The classification of Hindi-English code-mixed data into Hindi, English, and ambiguous words is done. Popular word embedding features were used for the representation of each word. The word embedding features and character embedding features have been used. The proposed method helps in identifying the word context by extracting the intent of user for using the ambiguous word in code-mixed sentence. Extensive experiments reveal that the artificial immune systems-based classification model outperforms competitive models especially when there are some ambiguous words in the social media data. [ABSTRACT FROM AUTHOR]

Graphical abstract Material Handling System. Highlights • A hybrid multi-objective optimization algorithm derived from immunological and biological evolution concepts is proposed. • An optimization approach integrating the proposed algorithm with an industrial-grade simulator is implemented. • The optimization approach’s performance is examined via numerical benchmark problems and a real-life simulation study. • The results reveal its ability of helping management to find near optimal system operating conditions and parameters. Abstract Optimization of complex real-world problems often involves multiple objectives to be considered simultaneously. These objectives are often non-commensurable and competing. For example, material handling is a vital element of industrial processes, which involves a variety of operations including the movement, storage and control of materials throughout the processes of manufacturing, distribution, and disposal while having to satisfy multiple objectives. Having an efficient and effective material handling system (MHS) is of great importance to various industries, such as manufacturing and logistics industries, for maintaining and facilitating a continuous flow of materials through the workplace and guaranteeing that required materials are available when needed. In this paper, a hybrid multi-objective optimization algorithm largely based on Artificial Immune Systems (AIS) is applied to simulation-based optimization of material handling system. This proposed algorithm hybridizes the AIS with the Genetic Algorithm (GA) by incorporating the crossover operator derived from the biological evolution. The reason behind such hybridization is to further enhance the diversity of the clone population and the convergence of the algorithm. In this paper, other than conducting numerical experiments to assess the performance of the proposed algorithm by using several benchmark problems, the proposed algorithm is also applied to optimize a multi-objective simulation-based problem on a material handling system in order to demonstrate the applicability of the proposed algorithm in real-life cases. The results show that for most cases the proposed algorithm outperforms the other benchmark algorithms especially in terms of solution diversity. [ABSTRACT FROM AUTHOR]

• Fast learning and data imputation using neighbour artificial immune system with a genetic algorithm. • Sustained and reliable predictions as the number of missing data increases in the datasets. • Sustained or improve recommendations of items to users from common methods used in collaborative filtering as missing data increases. In collaborative filtering, one of the main challenges that researchers face is sparseness in the data, which is caused by users rating fewer items as the number of items increase in the dataset. The effect is poor predictions and recommendations of items to users by expert and intelligent systems like the recommender system. This paper proposes a Nearest Neighbour Artificial Immune System with a Genetic Algorithm (NNAISGA) to perform fast data imputations to reduce sparseness. The main impact is sustained and reliable predictions as the number of missing data increases in the dataset. The second benefit is to help sustain or improve recommendations of items to users from common methods used in collaborative filtering such as the User-based, Item-based, Slope-one, Tendencies-based and Non-Negative Matrix Factorisation (NNMF) methods. We show that NNAISGA is the desired method for learning and imputation over the traditional genetic algorithm. The findings show that using the NNAISGA as a fast imputation method yields promising results. All of the methods, except for the User-based method, show significant improvements or sustained accuracies in terms of predictions and recommendations. [ABSTRACT FROM AUTHOR]

• HABC is proposed for breast cancer diagnosis where ABC is hybridized with modified AIS. • Tabu based employee bee phase is proposed that improves the local search of ABC. • HABC is used for selection of input features and tuning of parameters of ANN model. • HABC is tested with resilient backpropagation, Levenberg Marquardt, gradient descent. • HABC-RP achieved high accuracy of 99.14% and low connections of 12.40 using WBCD. Breast cancer is the most commonly occurring cancer among women, which contributes to the global death rate. The key to increasing the survival rate of affected patients is early diagnosis along with appropriate treatments. Manual methods for breast cancer diagnosis fail due to human errors, inaccurate diagnoses, and are time-consuming when demands are high. Intelligent systems based on Artificial Neural Network (ANN) for automated breast cancer diagnosis are powerful due to their strong decision-making capabilities in complicated cases. Artificial Bee Colony, Artificial Immune System, and Bacterial Foraging Optimization are swarm intelligence algorithms that solve combinatorial optimization problems. This paper proposes two novel hybrid Artificial Bee Colony (ABC) optimization algorithms that overcome the demerits of standard ABC algorithms. First, this paper proposes a hybrid ABC approach called HABC, in which the standard ABC optimization is hybridized with a modified clonal selection algorithm of the Artificial Immune System that eliminates the poor exploration capabilities of standard ABC optimization. Further, this paper proposes a novel hybrid Artificial Bee Colony (Hybrid ABC) optimization where the strong explorative capabilities of the chemotaxis phase of the bacterial foraging optimization are integrated with a spiral model-based exploitative phase of the ABC by which the proposed Hybrid ABC overcomes the demerits of poor exploration and exploitation of the standard ABC algorithm. In this work, the two proposed hybrid approaches were used in concurrent feature selection and parameter optimization of an ANN model. The proposed algorithm is implemented using various back-propagation algorithms, including resilient back-propagation (HABC-RP and Hybrid ABC-RP), Levenberg Marquart (HABC-LM and Hybrid ABC-LM), and momentum-based gradient descent (HABC-MGD and Hybrid ABC-GD) for parameter tuning of ANN. The Wisconsin breast cancer dataset was used to evaluate the performance of the proposed algorithms in terms of accuracy, complexity, and computational time. The mean accuracy of the proposed HABC-RP was 99.14% and 99.54% for Hybrid ABC which is better than the results found in the existing literature. HABC-RP attained a sensitivity of 98.32%, a specificity of 99.63%, and a precision of 99.38% whereas Hybrid ABC attained sensitivity of 99.08% and Specificity of 99.81%. HABC-RP and Hybrid ABC-RP yielded high accuracy with a low complexity ANN structure compared to other variants. After evaluation, interestingly it is found that the Hybrid ABC-RP has achieved the highest mean accuracy of 99.54% with low complexity of 10.25 mean connections when compared to other variants proposed in this paper. It can be concluded that the concurrent selection of input features and tuning of parameters of ANN plays a vital role in increasing the accuracy of a breast cancer diagnosis. The proposed HABC-RP and Hybrid ABC-RP showed better results when compared to the existing breast cancer diagnosis systems taken for comparison. In the future, the proposed two-hybrid approaches can be used to generate optimal thresholds for the segmentation of tumors in abnormal images. HABC and Hybrid ABC can be used for tuning the parameters of various classifiers. [ABSTRACT FROM AUTHOR]

Abstract: The Negative Selection Algorithm developed by Forrest et al. was inspired by the way in which T-cell lymphocytes mature within the thymus before being released into the blood system. The mature T-cell lymphocytes exhibit an interesting characteristic, in that they are only activated by non-self cells that invade the human body. The Negative Selection Algorithm utilises an affinity matching function to ascertain whether the affinity between a newly generated (NSA) T-cell lymphocyte and a self-cell is less than a particular threshold; that is, whether the T-cell lymphocyte is activated by the self-cell. T-cell lymphocytes not activated by self-sells become mature T-cell lymphocytes. A new affinity matching function termed the feature-detection rule is introduced in this paper. The feature-detection rule utilises the interrelationship between both adjacent and non-adjacent features of a particular problem domain to determine whether an antigen is activated by an artificial lymphocyte. The performance of the feature-detection rule is contrasted with traditional affinity matching functions, currently employed within Negative Selection Algorithms, most notably the r-chunks rule (which subsumes the r-contiguous bits rule) and the hamming distance rule. This paper shows that the feature-detection rule greatly improves the detection rates and false alarm rates exhibited by the NSA (utilising the r-chunks and hamming distance rule) in addition to refuting the way in which permutation masks are currently being applied in artificial immune systems. [Copyright &y& Elsevier]

Abstract: This paper describes the biological and theoretical foundations of a new Artificial Immune System the Receptor Density Algorithm. The algorithm is developed with inspiration from T cell signalling processes and has application in anomaly detection. Connections between the Receptor Density Algorithm and kernel density estimation with exponential smoothing are demonstrated. Finally, the paper evaluates the algorithm’s performance on two types of anomaly detection problem. [Copyright &y& Elsevier]

Abstract: Free-form parametric surfaces are common tools nowadays in many applied fields, such as Computer-Aided Design & Manu- facturing (CAD/CAM), virtual reality, medical imaging, and many others. A typical problem in this setting is to fit surfaces to 3D noisy data points obtained through either laser scanning or other digitizing methods, so that the real data from a physical object are transformed back into a fully usable digital model. In this context, the present paper describes an immunological- based approach to perform this process accurately by using the classical free-form Bézier surfaces. Our method applies a powerful bio-inspired paradigm called Artificial Immune Systems (AIS), which is receiving increasing attention from the sci- entific community during the last few years because of its appealing computational features. The AIS can be understood as a computational methodology based upon metaphors of the biological immune system of humans and other mammals. As such, there is not one but several AIS algorithms. In this chapter we focus on the clonal selection algorithm (CSA), which explicitly takes into account the affinity maturation of the immune response. The paper describes how the CSA algorithm can be effectively applied to the accurate fitting of 3D noisy data points with Bézier surfaces. To this aim, the problem to be solved as well as the main steps of our solving method are described in detail. Some simple yet illustrative examples show the good performance of our approach. Our method is conceptually simple to understand, easy to implement, and very general, since no assumption is made on the set of data points or on the underlying function beyond its continuity. As a consequence, it can be successfully applied even under challenging situations, such as the absence of any kind of information regarding the underlying function of data. [Copyright &y& Elsevier]

Typical artificial immune system (AIS) operators such as hypermutations with mutation potential and ageing allow to efficiently overcome local optima from which evolutionary algorithms (EAs) struggle to escape. Such behaviour has been shown for artificial example functions constructed especially to show difficulties that EAs may encounter during the optimisation process. However, no evidence is available indicating that these two operators have similar behaviour also in more realistic problems. In this paper we perform an analysis for the standard NP-hard Partition problem from combinatorial optimisation and rigorously show that hypermutations and ageing allow AISs to efficiently escape from local optima where standard EAs require exponential time. As a result we prove that while EAs and random local search (RLS) may get trapped on 4/3 approximations, AISs find arbitrarily good approximate solutions of ratio (1 + ϵ) within n (ϵ − (2 / ϵ) − 1) (1 − ϵ) − 2 e 3 2 2 / ϵ + 2 n 3 2 2 / ϵ + 2 n 3 function evaluations in expectation. This expectation is polynomial in the problem size and exponential only in 1 / ϵ. To the best of our knowledge this is the first time performance guarantees of any AIS are proven for a classical combinatorial optimisation problem. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective immune memetic algorithm to the challenge of solving the Frequency Assignment Problem (FAP) in cellular networks seeking the minimization of the network's total interference, the maximum interference and the number of used frequencies. The originality of the proposed approach lies in integrating a FAP-specific local search into its evolutionary process instead of crossover and mutation, as well as a guided diversification strategy for better performances. Moreover, the algorithm is supplemented with a clonal selection, inherited from Artificial Immune Systems (AIS), which aims to improve the algorithm exploration and exploitation abilities. Computational experiments performed over COST259 instances show the efficiency of the newly proposed evolutionary multi-objective algorithm and corroborated by the comparisons we did with the most frequently referred algorithm in the related literature. Furthermore, the effect of the main parameters and the interaction between them is analyzed using statistical tools. [ABSTRACT FROM AUTHOR]

In a previous research, a multi-agent artificial immune system for network intrusion detection and classification was proposed and tested, where a multi-layer detection and classification process was executed on each agent, for each host in the network. In this paper, we show the experiments that were held to chose the appropriate classifiers by testing different classifiers and comparing them to increase the detection accuracy and obtain more information on the detected anomalies. It will be shown that no single classifier should be used for all types of attacks, due to different classification rates obtained. This is due to attacks representations in the train set and dependency between features used to detect them. It will also be shown that a basic and simple classifier such as Naive Bayes has better classification results in the case of low-represented attacks, and the basic decision trees such as Naive-Bayes Tree and Best-First Tree give very good results compared to well-known J48 (Weka implementation of C4.5) and Random Forest decision trees. Based on these experiments and their results, Naive Bayes and Best-First tree classifiers were selected to classify the anomaly-detected traffic. It was shown that in the detection phase, 90% of anomalies were detected, and in the classification phase, 88% of false positives were successfully labeled as normal traffic connections, and 79% of DoS and Probe attacks were labeled correctly, mostly by NB, NBTree, and BFTree classifiers. [ABSTRACT FROM AUTHOR]

This paper aims to document the application of a new generation of artificial immune systems (AIS) in fault detection and isolation problems. These kind of algorithms are able to explore normal and anomalous behavior evidences, however, they may often require a more explicit prior knowledge provided by experts, usually difficult to obtain in some practical cases. Thus, many immune inspired approaches applied to fault detection and isolation (FDI) in the literature are based on negative selection algorithms. Considering these points, this work presents a review on three AIS approaches. Once reviewed and contextualized, the evaluated techniques are properly adjusted considering their main parameters and ways of processing data, and then, applied to a case study of fault detection and isolation in order to provide a performance analysis of these techniques, according to their applicability to these problems. [ABSTRACT FROM AUTHOR]

In this paper we investigate the efficiency of two immunological algorithms (CLONALG and opt-IA) in the evolution of Boolean functions suitable for use in cryptography. Although in its nature a combinatorial problem, we experiment with two representations of solutions, namely, the bitstring and the floating point based representation. The immunological algorithms are compared with two commonly used evolutionary algorithms – genetic algorithm and evolution strategy. To thoroughly investigate these algorithms and representations, we use four different fitness functions that differ in the number of parameters and difficulty. Our results indicate that for smaller dimensions immunological algorithms behave comparable with evolutionary algorithms, while for the larger dimensions their performance is somewhat worse. When considering only immunological algorithms, opt-IA outperforms CLONALG in most of the experiments. The difference in the representation for those algorithms is also clear where floating point works better with smaller problem sizes and bitstring representation works better for larger Boolean functions. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to detect and classify background noise in speech sentences based on the negative selection algorithm and dual-tree complex wavelet transform. The energy of the complex wavelet coefficients across five wavelet scales are used as input features. Afterward, the proposed algorithm identifies whether the speech sentence is, or is not, corrupted by noise. In the affirmative case, the system returns the type of the background noise amongst the real noise types considered. Comparisons with classical supervised learning methods are carried out. Simulation results show that the artificial immune system proposed overcomes classical classifiers in accuracy and capacity of generalization. Future applications of this tool will help in the development of new speech enhancement or automatic speech recognition systems based on noise classification. [ABSTRACT FROM AUTHOR]

• Offline Handwritten signature verification systems suffer from the lack of genuine signature samples. • We propose a data augmentation algorithm that generates synthetic genuine signature features. • We propose a new signature shape descriptor based on multi-oriented local difference features. • Results obtained on benchmark datasets confirm the effectiveness of proposed methods. Identity verification based on offline handwritten signatures is required in various financial and administrative domains using paper documents. Commonly, the performance of signature verification systems is altered by the lack of sufficient authentic signatures. To overcome such shortcoming, various data augmentation schemes were proposed to generate synthetic samples. Since both genuine and synthetic signatures undergo a feature generation process inside the verification system, we propose to directly apply data augmentation on features instead of the signature image. Such an approach can give more pertinent synthetic features, than those derived from synthetic signatures. The proposed features generator is based on mutation, cloning, and resources competition mechanisms of artificial immune systems. It is evaluated on two descriptors by using SVM classifier in the verification step. As a first descriptor, we introduce the multi-oriented local difference features. The second descriptor is the histogram of templates which is used for comparison aims, since it showed satisfactory performance for handwritten signature characterization. Experiments conducted on CEDAR, GPDS-300, and MCYT-75 datasets, highlight the effectiveness of synthetic features, which allow at least an improvement of 8% when the verification system is developed by using one real signature. Also, the proposed MLDF outperforms both HOT and several state of the art features including LBP and SigNet. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology to solve the reconfiguration problem of electrical distribution systems (EDSs) with variable demand, using the artificial immune algorithm Copt-aiNet (Artificial Immune Network for Combinatorial Optimization). This algorithm is an optimization technique inspired by immune network theory (aiNet). The reconfiguration problem with variable demand is a complex problem of a combinatorial nature. The goal is to identify the best radial topology for an EDS in order to minimize the cost of energy losses in a given operation period. A specialized sweep load flow for radial systems was used to evaluate the feasibility of the topology with respect to the operational constraints of the EDS and to calculate the active power losses for each demand level. The algorithm was implemented in C++ and was evaluated using test systems with 33, 84, and 136 nodes, as well as a real system with 417 nodes. The obtained results were compared with those in the literature in order to validate and prove the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a new artificial immune algorithm with continuous-learning, which is inspired by the biological immune system, to realize the voltage diagnosis in electrical distribution systems. This conception allows one to compose a diagnosis system that can continuously learn without reinitialization when new disturbances occur due to the evolution of the electrical system. Two artificial immune algorithms, which are the negative selection algorithm and the clonal selection algorithm, are used for the pattern recognition process and the learning process, respectively. The principal application of this new method aids the operation during failures, supervises the protection system, and can evolve with the power systems to continuously acquire new knowledge. This new methodology has a direct impact in the area of diagnosis in electrical systems, as well as, in the pattern recognition problem, because the main contribution and novelty of this method is the continuous learning capability, which enables the system to learn unknown patterns without having to restart the knowledge. This is the major advantage of this methodology. To evaluate the efficiency and performance of this new method, failure simulations were performed in a real distribution system with 134 buses using the EMTP software. The results show robustness and efficiency. [ABSTRACT FROM AUTHOR]

Swarm robotics is concerned with the decentralised coordination of multiple robots having only limited communication and interaction abilities. Although fault tolerance and robustness to individual robot failures have often been used to justify the use of swarm robotic systems, recent studies have shown that swarm robotic systems are susceptible to certain types of failure. In this paper we propose an approach to self-healing swarm robotic systems and take inspiration from the process of granuloma formation, a process of containment and repair found in the immune system. We use a case study of a swarm performing team work where previous works have demonstrated that partially failed robots have the most detrimental effect on overall swarm behaviour. We have developed an immune inspired approach that permits the recovery from certain failure modes during operation of the swarm, overcoming issues that effect swarm behaviour associated with partially failed robots. [ABSTRACT FROM AUTHOR]

Various algorithms inspired by evolutionary and physical processes have been extensively applied in solving complex construction engineering optimization problems. In this paper, Artificial Immune Systems (AIS), a computational approach inspired by the processes of human immune system, is introduced in terms of its basic mechanisms and its applications in construction engineering. Specifically, Clonal Selection Algorithm (CSA), one of main algorithms that form AIS, is based on clonal selection process of the immune system which includes the selection, hypermutation, and receptor editing processes. We discuss the CSA in detail and present its application in the classic construction optimization problem, construction site utilization planning (CSUP), which is the decision making process for identifying the most optimal layout of temporary facilities designed to support the construction process. When applied to a test case published in research literature, we found that CSA shows a robust capacity to search the solution space effectively and efficiently. [ABSTRACT FROM AUTHOR]

SuperParent-One-Dependence Estimators (SPODEs) represent a family of semi-naive Bayesian classifiers which relax the attribute independence assumption of Naive Bayes (NB) to allow each attribute to depend on a common single attribute (superparent). SPODEs can effectively handle data with attribute dependency but still inherent NB׳s key advantages such as computational efficiency and robustness for high dimensional data. In reality, determining an optimal superparent for SPODEs is difficult. One common approach is to use weighted combinations of multiple SPODEs, each having a different superparent with a properly assigned weight value ( i.e ., a weight value is assigned to each attribute). In this paper, we propose a self-adaptive SPODEs, namely SODE, which uses immunity theory in artificial immune systems to automatically and self-adaptively select the weight for each single SPODE. SODE does not need to know the importance of individual SPODE nor the relevance among SPODEs, and can flexibly and efficiently search optimal weight values for each SPODE during the learning process. Extensive experiments and comparisons on 56 benchmark data sets, and validations on image and text classification, demonstrate that SODE outperforms state-of-the-art weighted SPODE algorithms and is suitable for a wide range of learning tasks. Results also confirm that SODE provides an appropriate balance between runtime efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Biological immunity is a natural system that protects a host organism against disease causing elements threatening its normal functioning. It offers many interesting features that inspired the design of Artificial Immune Systems (AIS) to solve several kinds of engineering problems. As a manufacturing system can be assimilated to a host organism, while process anomalies (e.g. faults, errors and failures) can be considered as disease causing elements, biological immunity is particularly inspiring approaches for fault detection, diagnosis and recovery (FDDR). Although many interesting works and different adaptations were suggested, we are not aware of any recent survey that would aim at reviewing works, synthesizing modeling approaches and reporting on results in this field. This paper provides a recent survey and an analysis framework to fill in this gap. After a first part overviewing FDDR needs and requirements, we introduce biological immunity and highlight the main concepts and mechanisms that are particularly relevant to FDDR problems. The numerous works analyzed distinguish three categories of AIS: one-signal (for positive and negative selection) based approaches, two-signal (for danger and NK) based approaches and immune network based approaches. We suggest a possible architecture for FDDR systems, and organize the immune system concepts, components and mechanisms in such a way to show how they are applied for each of the detection, diagnosis and recovery tasks. Our analysis allows an overview of current technical and methodical developments in this field and foresight of future research perspectives. [ABSTRACT FROM AUTHOR]

This paper proposes an immune-inspired algorithm to the problem of minimising the makespan on unrelated parallel machines, with sequence dependent setup times. The initial population is generated through the construction phase of the Greedy Randomised Adaptive Search Procedure (GRASP). An evaluation function is proposed to help the algorithm escape from local optima. A Variable Neighbourhood Descent (VND) local search heuristic, which makes significant use of the characteristics of the problem, is proposed as a somatic hypermutation operator to accelerate the convergence of the algorithm. A population re-selection operator, which strategically keeps good quality solutions with a high level of dispersion in the search space, is also proposed. The experiments performed show that the proposed algorithm enables better results than those reported in recent literature studies. [ABSTRACT FROM AUTHOR]

Naive Bayes (NB) is a popular machine learning tool for classification, due to its simplicity, high computational efficiency, and good classification accuracy, especially for high dimensional data such as texts. In reality, the pronounced advantage of NB is often challenged by the strong conditional independence assumption between attributes, which may deteriorate the classification performance. Accordingly, numerous efforts have been made to improve NB, by using approaches such as structure extension, attribute selection, attribute weighting, instance weighting, local learning and so on. In this paper, we propose a new Artificial Immune System (AIS) based self-adaptive attribute weighting method for Naive Bayes classification. The proposed method, namely AISWNB, uses immunity theory in Artificial Immune Systems to search optimal attribute weight values, where self-adjusted weight values will alleviate the conditional independence assumption and help calculate the conditional probability in an accurate way. One noticeable advantage of AISWNB is that the unique immune system based evolutionary computation process, including initialization, clone, section, and mutation, ensures that AISWNB can adjust itself to the data without explicit specification of functional or distributional forms of the underlying model. As a result, AISWNB can obtain good attribute weight values during the learning process. Experiments and comparisons on 36 machine learning benchmark data sets and six image classification data sets demonstrate that AISWNB significantly outperforms its peers in classification accuracy, class probability estimation, and class ranking performance. [ABSTRACT FROM AUTHOR]

In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space issues of qualitative model learning has been investigated. More importantly, to further improve the efficiency of QML-AiNet, we also modify the mutation operator according to the features of discrete qualitative model space. Experimental results show that the performance of QML-AiNet is comparable to QML-CLONALG, a QML system using the clonal selection algorithm (CLONALG). More importantly, QML-AiNet with the modified mutation operator can significantly improve the scalability of QML and is much more efficient than QML-CLONALG. [ABSTRACT FROM AUTHOR]

Data fitting with B-splines is a challenging problem in reverse engineering for CAD/CAM, virtual reality, data visualization, and many other fields. It is well-known that the fitting improves greatly if knots are considered as free variables. This leads, however, to a very difficult multimodal and multivariate continuous nonlinear optimization problem, the so-called knot adjustment problem. In this context, the present paper introduces an adapted elitist clonal selection algorithm for automatic knot adjustment of B-spline curves. Given a set of noisy data points, our method determines the number and location of knots automatically in order to obtain an extremely accurate fitting of data. In addition, our method minimizes the number of parameters required for this task. Our approach performs very well and in a fully automatic way even for the cases of underlying functions requiring identical multiple knots, such as functions with discontinuities and cusps. To evaluate its performance, it has been applied to three challenging test functions, and results have been compared with those from other alternative methods based on AIS and genetic algorithms. Our experimental results show that our proposal outperforms previous approaches in terms of accuracy and flexibility. Some other issues such as the parameter tuning, the complexity of the algorithm, and the CPU runtime are also discussed. [ABSTRACT FROM AUTHOR]

The amount of online transactions is growing these days to a large number. A big portion of these transactions contains credit card transactions. The growth of online fraud, on the other hand, is notable, which is generally a result of ease of access to edge technology for everyone. There has been research done on many models and methods for credit card fraud prevention and detection. Artificial Immune Systems is one of them. However, organizations need accuracy along with speed in the fraud detection systems, which is not completely gained yet. In this paper we address credit card fraud detection using Artificial Immune Systems (AIS), and introduce a new model called AIS-based Fraud Detection Model (AFDM). We will use an immune system inspired algorithm (AIRS) and improve it for fraud detection. We increase the accuracy up to 25%, reduce the cost up to 85%, and decrease system response time up to 40% compared to the base algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Recently, several instance selection-based methods have been presented to solve the multiple-instance learning (MIL) problem. The basic idea is converting MIL into standard supervised learning by selecting some representative instance prototypes from the training set. However, training examples are not single instances but bags composed of one or more instances in MIL, so the computational complexity is often very high. Previous methods consider this issue only from the perspective of instance selection not from that of example selection. In this paper, we try to address this issue via combining example selection with instance selection. Three general example selection methods are derived by adapting three immune-inspired algorithms to MIL. Additionally, we propose a simple instance selection method for MIL based on the probability that an instance is positive given a set of negative instances. Our example selection methods are combined with the new MIL method and other previous instance selection-based ones as a preprocessing step. The theoretical analysis and empirical results show that our MIL method is competitive to the state-of-the-art and the proposed example selection methods could significantly speed up various instance selection-based MIL methods with slightly weakening their performance or even strengthening it. [Copyright &y& Elsevier]

Artificial immune detectors are the basic recognition components of immune systems. Traditionally, the candidate non-self detectors are compared with the whole self training set to eliminate self-reactive ones in negative selection algorithms (NSAs). However, the training process has low efficiency due to the exhausting comparisons. Furthermore, it can be more efficient if we straightforwardly generate self-detectors based on the available self samples to avoid the overwhelmed comparisons. In the paper, a new detector training algorithm is proposed. Firstly, the self training set is enlarged by the label propagation algorithm (LPA) using both labeled and unlabeled samples; and then the newly labeled samples is evaluated based on noisy learning theory to remove the unqualified ones. Finally self-detectors are directly generated at the locations of self samples. The theoretical analysis demonstrated that the time complexity of our algorithm is much reduced, especially that the exponential relationship between self size and time complexity in traditional NSAs is eliminated. The experimental results showed that: not only the time cost of detector training, but also the detection accuracy is improved. [ABSTRACT FROM AUTHOR]

Abstract: Plug-in hybrid electric vehicles are new alternatives that meet the efforts to develop more sustainable means of transportation and decreasing oil dependence. However, this trend could bring negative consequences to electric power systems, such as limits violations, power losses increasing, harmonics, and thermal limits violation on distribution transformers and conductors. To cope with some of those problems, this paper proposes a recharging process with the help of Artificial Immune Systems, so the voltage level in all buses is kept within the operational limits and no overload is observed. For this sake, besides distributing the load along the recharging period, capacitors installation to decrease electric losses and improve voltage levels during vehicles recharging is also considered. The results obtained render the proposed methodology as effective. Two IEEE sample systems are used to test the idea, so the results may be reproduced for research purposes. [Copyright &y& Elsevier]

Abstract: As one of the emerging algorithms in the field of artificial immune systems (AIS), the dendritic cell algorithm (DCA) has been successfully applied to a number of challenging real-world problems. However, one criticism is the lack of a formal definition, which could result in ambiguity for understanding the algorithm. Moreover, previous investigations have mainly focused on its empirical aspects. Therefore, it is necessary to provide a formal definition of the algorithm, as well as to perform runtime analyses to reveal its theoretical aspects. In this paper, we define the deterministic version of the DCA, named the dDCA, using set theory and mathematical functions. Runtime analyses of the standard algorithm and the one with additional segmentation are performed. Our analysis suggests that the standard dDCA has a runtime complexity of for the worst-case scenario, where n is the number of input data instances. The introduction of segmentation changes the algorithm''s worst case runtime complexity to , for DC population size N with size of each segment z. Finally, two runtime variables of the algorithm are formulated based on the input data, to understand its runtime behaviour as guidelines for further development. [Copyright &y& Elsevier]

Abstract: This paper describes an immune-inspired system based on an alternate theory about the self–nonself distinction theory, which defines the negative selection process as a mechanism of a fuzzy system based on the affinity between antigen and T-cells. This theory may provide a decision making tool which improves the generation of detectors or even define new data monitoring in order to detect an extreme variation of the system behavior, which means anomalies occurrences. Through these algorithms, tests are performed to detect faults of a DC motor. Upon detection of faults, a participatory clustering algorithm is used to classify these faults and tested to obtain the best set of parameters to achieve the most accurate clustering for these tests in the application being discussed in the article. [Copyright &y& Elsevier]

Abstract: This paper presents a new methodology for installing monitors into a distribution network to optimally and robustly monitor the voltage sag performance of the entire network. The developed methodology allows distribution network planners to specify budgetary requirements and future loading forecasts and output a robust set of monitoring locations for voltage sag performance monitoring. The research utilizes an artificial immune system optimization (AIS) algorithm to discover a range of near-optimal monitoring locations based on minimization of expected equipment trips at a bus. An estimate for the probable number of equipment trips is defined using a new probabilistic measure known as sag trip probability (STP). The optimized solutions are analyzed for robustness across a range of uncertain future network loading and topology scenarios. The results show the methodology applied to a 295 bus generic distribution network with uncertain load growth for the next 15 years. [Copyright &y& Elsevier]

Abstract: Securing ad hoc routing protocols for MANETs is a significant challenge due to number of reasons: (1) mobility results in continuously changing network topology – the premise of stable self or non-self is void, (2) the proposed security solution must be lightweight so that it can be deployed on resource constrained mobile nodes, and (3) the solution should provide high detection accuracy and low false positive rate. The major contribution of this paper is a hybrid AIS model – combining the relevant features of classical self/non-self paradigm with the emerging danger theory paradigm – that has the capability to meet the above-mentioned challenges of the MANET environment. As a case study, we use our hybrid model to develop a power aware security framework for BeeAdHoc– a well-known bio-inspired routing protocol. We have realized our framework in ns-2 simulator. We have also developed an attacker framework in ns-2 that has the capability to launch a number of Byzantine attacks on BeeAdHoc. The results of our experiments show that our proposed framework meets all its requirements: (1) the adaptive learning because of changing self/non-self, (2) high detection accuracy and low false positive rate, (3) lightweight in terms of processing and communication overheads, and (4) better or comparable performance compared with non-secure versions of existing state-of-the-art MANET routing protocols –DSR and AODV. We have also compared our hybrid AIS model with self/non-self, danger theory and a conventional anomaly detection system to show its merits over these schemes. Finally, we propose an extension of the framework for securing DSR. [Copyright &y& Elsevier]

Abstract: This paper presents a selective SVM ensemble based on clustering Algorithm and a new AIS based on real-valued NS (RNS) algorithm to localize the faults of analogue circuits with small samples. The fault diagnosis experiments on a continuous-time state-variable filter circuit using SVM and AIS are carried out. Experimental results demonstrate that AIS is a more effective method to fault diagnosis with small samples. [Copyright &y& Elsevier]

Abstract: In real-valued negative selection algorithm, the variability of self sample would result in the holes on the boundary between the self and non-self region and the deceiving anomalies hidden in the self region. This paper analyzes the reason for the difficulty in handling these problems by traditional evolved detectors, and then proposes a method of evolving boundary detectors to solve them. This method uses an improved detector generation algorithm based on evolutionary search to generate boundary detectors. The boundary detectors constructed by an aggressive interpretation are allowed to cover a part of self region. The aggressiveness controlled by boundary threshold can convert some volume of self sample into the fitness of boundary detector. This makes them enable to eliminate the holes on the boundary and have an opportunity to detect the deceiving anomalies hidden in the self region. Experiments are carried out using both 2-dimensional dataset and real world dataset. The former was designed to demonstrate intuitively that boundary detectors can cover the holes on the boundary, while the latter was to show that boundary detectors can detect the deceiving anomalies. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an artificial immune system approach to the detection and diagnosis of faults in the stator and rotor circuits of an induction machine is presented. The proposed technique requires the measurement of two stator currents to compute their αβ representation before and after a fault condition. It is verified that for different faults, different patterns are generated by the vector αβ representation, helping to construct a characteristic image of the operating condition of the induction machine. A pattern recognition algorithm inspired by how the immune system operates throughout the body is proposed to identify and classify the fault condition. According to the proposed methodology, there is no need to know the details of machine operation in a certain regime and all phenomena and effects resulting from the machine operating in this regime are taken into account. Several experimental results obtained on 2.2kW and 3.2kW three-phase induction machines are presented and discussed to validate the methodology, verifying its good performance in preventive fault detection. [ABSTRACT FROM AUTHOR]

Abstract: A combined short-term learning (STL) and long-term learning (LTL) approach to solving mobile-robot navigation problems is presented and tested in both the real and virtual domains. The LTL phase consists of rapid simulations that use a genetic algorithm to derive diverse sets of behaviours, encoded as variable sets of attributes, and the STL phase is an idiotypic artificial immune system. Results from the LTL phase show that sets of behaviours develop very rapidly, and significantly greater diversity is obtained when multiple autonomous populations are used, rather than a single one. The architecture is assessed under various scenarios, including removal of the LTL phase and switching off the idiotypic mechanism in the STL phase. The comparisons provide substantial evidence that the best option is the inclusion of both the LTL phase and the idiotypic system. In addition, this paper shows that structurally different environments can be used for the two phases without compromising transferability. [Copyright &y& Elsevier]

Abstract: In this paper, an Evolutionary Memetic Algorithm (EMA), which uses a local search intensity scheme to complement the global search capability of Evolutionary Algorithms (EAs), is proposed for rule extraction. Two schemes for local search are studied, namely EMA-GA, which uses a micro-Genetic Algorithm-based (GA) technique, and EMA-AIS, which is inspired by Artificial Immune System (AIS) and uses the clonal selection for cell proliferation. The evolutionary memetic algorithm is complemented with the use of a variable-length chromosome structure, which allows the flexibility to model the number of rules required. In addition, advanced variation operators are used to improve different aspects of the algorithm. Real world benchmarking problems are used to validate the performance of EMA and results from simulations show the proposed algorithm is effective. [Copyright &y& Elsevier]

Abstract: Routing problems constitute an important class of problems because they are hard to solve and have a wide variety of real-world applications. Two of the most well-studied routing problems are the traveling salesman problem (TSP) and the multiple traveling salesmen problem (MTSP). This paper proposes the use of a neuro-immune self-organizing network to solve both, TSP and MTSP. The algorithms are introduced and a brief review of the literature on self-organizing networks, applied separately to each of these problems, is performed. To assess the performance of the proposed algorithms, a number of tests are conducted on benchmark data, and the results are directly compared with those of similar works. The results obtained show a better performance in terms of the best solution found for many instances, and an equivalent average performance. The main advantages of the proposed approach are its network constructive architecture and the use of a single growing network to solve the MTSP. [Copyright &y& Elsevier]

Abstract: In this paper, we have made medical application of a new artificial immune system named the information gain-based artificial immune recognition system (IG-AIRS) which is minimized the negative effects of taking into account all attributes in calculating Euclidean distance in shape-space representation which is used in many artificial immune systems. For medical data, microorganism dataset was applied in the performance analysis of our proposed system. Microorganism dataset was obtained using Cyranose 320 electronic nose. Our proposed system reached 92.35% classification accuracy with five-fold cross validation method. This result ensured that IG-AIRS would be helpful in classification of microorganism species based on laboratory tests, and would open the way to various microorganism species determine support by using electronic nose. [Copyright &y& Elsevier]

Abstract: In this paper, we have made medical application of a new artificial immune system named the information gain based artificial immune recognition system (IG-AIRS) which minimizes the negative effects of taking into account all attributes in calculating Euclidean distance in shape–space representation which is used in many artificial immune systems. For medical data, thyroid disease data set was applied in the performance analysis of our proposed system. Our proposed system reached 95.90% classification accuracy with 10-fold CV method. This result ensured that IG-AIRS would be helpful in diagnosing thyroid function based on laboratory tests, and would open the way to various ill diagnoses support by using the recent clinical examination data, and we are actually in progress. [Copyright &y& Elsevier]

Abstract: Artificial Immune Systems (AIS) are a type of intelligent algorithm inspired by the principles and processes of the human immune system. In the last decade, applications of AIS have been studied in various fields. In the application of change/anomaly detection, negative selection algorithms of AIS have been successfully applied. However, negative selection algorithms are not appropriate for multi-class classification problems, because they do not have a mechanism to minimize the danger of overfitting and oversearching. In this paper, we propose a new algorithm to overcome this drawback and to extend the application area of negative selection algorithms to multi-class classification. The algorithm we propose is named Artificial Negative Selection Classifier (ANSC). We investigate the tolerance of ANSC against noise, and introduce a method to reduce the effect of noise into ANSC. The accuracy and data reduction are compared with those from the Artificial Immune Recognition System (AIRS), which is a well known and effective classifier of AIS. The results show that our algorithm is useful for classification problems and the reduction of the noise effect. [Copyright &y& Elsevier]

Abstract: This paper presents a novel evolutionary algorithm inspired by protein/substrate binding exploited in enzyme genetic programming (EGP) and artificial immune networks. The immune network-inspired evolutionary algorithm has been developed in direct response to an application in clinical neurology, the diagnosis of Parkinson’s disease. The inspiration for, and implementation of the algorithm is described and its performance to the application area considered. [Copyright &y& Elsevier]

Abstract: Artificial immune systems (AIS) constitute a relatively new area of bio-inspired computing. Biological models of the natural immune system, in particular the theories of clonal selection, immune networks and negative selection, have provided the inspiration for AIS algorithms. Moreover, such algorithms have been successfully employed in a wide variety of different application areas. However, despite these practical successes, until recently there has been a dearth of theory to justify their use. In this paper, the existing theoretical work on AIS is reviewed. After the presentation of a simple example of each of the three main types of AIS algorithm (that is, clonal selection, immune network and negative selection algorithms respectively), details of the theoretical analysis for each of these types are given. Some of the future challenges in this area are also highlighted. [Copyright &y& Elsevier]

Abstract: This paper proposes two new algorithms based on the clonal selection principle for the design of spreading codes for DS-CDMA. The first algorithm follows a multi-objective approach, generating complex spreading codes with “good” auto as well as cross-correlation properties. It also enables spreading code design with no restrictions on the number of users or code length. The algorithm maintains a repertoire of codes that are subject to cloning and undergo a process of affinity maturation to obtain better codes. Results indicate that the produced code sets lie very close to the theoretical Pareto front. A second penalty function-based constrained optimization algorithm based on clonal selection is proposed. It is applied to the design of spreading codes with pre-defined power spectral density requirement. The results suggest that the algorithm is capable of lowering significantly, the power spectra at undesired frequencies. Therefore, with the proposed algorithm, a DS-CDMA transmitter can, for the first time, selectively transmit power across the transmission bandwidth and adjust to jammers and other interferers. This study illustrates that using two stages of multi-objective and constrained optimization, using the proposed clonal selection algorithms, is an effective code design strategy. [Copyright &y& Elsevier]

Abstract: After a decade of research into the area of artificial immune systems, it is worthwhile to take a step back and reflect on the contributions that the paradigm has brought to the application areas to which it has been applied. Undeniably, there have been a lot of successful stories—however, if the field is to advance in the future and really carve out its own distinctive niche, then it is necessary to be able to illustrate that there are clear benefits to be obtained by applying this paradigm rather than others. This paper attempts to take stock of the application areas that have been tackled in the past, and ask the difficult question “was it worth it ?”. We then attempt to suggest a set of problem features that we believe will allow the true potential of the immunological system to be exploited in computational systems, and define a unique niche for AIS. [Copyright &y& Elsevier]

This paper presents a new method for constructing ensembles of classifiers based on immune network theory, one of the most interesting paradigms within the field of artificial immune systems. Ensembles of classifiers are a very interesting alternative to single classifiers when facing difficult problems. In general, ensembles are able to achieve better performance in terms of learning and generalisation error. Artificial immune system is a new paradigm within the field of bioinspired algorithms that mimics the behaviour of the natural immune system of animals to develop solutions for a given problem. Within artificial immune systems, one of the most innovative and appealing fields is immune network theory. We construct an immune network that constitutes an ensemble of classifiers. Using a neural network as base classifier we have compared the performance of this ensemble with five standard methods of ensemble construction. This comparison is made using 35 real-world classification problems from the UCI Machine Learning Repository. The results show that the proposed model exhibits a general advantage over the standard methods. [Copyright &y& Elsevier]

Abstract: Artificial immune systems (AISs) are biologically inspired problem solvers that have been used successfully as intrusion detection systems (IDSs). In this paper, we compare a genetic hacker with 12 evolutionary hackers based on particle swarm optimization (PSO) that have been effectively used as vulnerability analyzers (red teams) for AIS-based IDSs. Our results show that the PSO-based red teams that use Clerc''s constriction coefficient outperform those that do not. Our results also show that the three types of red teams (genetic, basic PSO, and PSO with the constriction coefficient) have distinct search behaviors that are complimentary. [Copyright &y& Elsevier]

From the point of view of information processing the immune system is a highly parallel and distributed intelligent system which has learning, memory, and associative retrieval capabilities. In this paper we present two immunity-based hybrid learning approaches for function approximation (or regression) problems that involve adjusting the structure and parameters of spatially localized models (e.g., radial basis function networks). The number and centers of the receptive fields for local models are specified by immunity-based structure adaptation algorithms, while the parameters of the local models, which enter in a linear fashion, are tuned separately using a least-squares method. The effectiveness of the procedure is demonstrated through a nonlinear function approximation problem and a nonlinear dynamical system modeling problem. [Copyright &y& Elsevier]