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In this paper, an overview of artificial immune systems (AIS) used in intrusion detection systems (IDS) is provided, along with a review of recent efforts in this field of cybersecurity. In particular, the focus is on the negative selection algorithm (NSA), a popular, prominent algorithm of the AIS domain based on the human immune system. IDS offer intrusion detection capabilities, both locally and in a network environment. The paper offers a review of recent solutions employing AIS in IDS, capable of detecting anomalous network traffic/breaches and operating system file infections caused by malware. A discussion regarding the reviewed research is presented with an analysis and suggestions for further research, and then the work is concluded. [ABSTRACT FROM AUTHOR]

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Manufacturing companies optimize logistics network routing to reduce transportation costs and operational costs in order to make profits in an extremely competitive environment. Therefore, the efficiency of logistics management in the supply chain and the quick response to customers' demands are treated as an additional source of profit. One of the warehouse operations for intelligent logistics network design, called cross-docking (CD) operations, is used to reduce inventory levels and improve responsiveness to meet customers' requirements. Accordingly, the optimization of a vehicle dispatch schedule is imperative in order to produce a routing plan with the minimum transport cost while meeting demand allocation. Methods: This paper developed a two-phase algorithm, called sAIS, to solve the vehicle routing problem (VRP) with the CD facilities and systems in the logistics operations. The sAIS algorithm is based on a clustering-first and routing-later approach. The sweep method is used to cluster trucks as the initial solution for the second phase: optimizing routing by the Artificial Immune System. Results: In order to examine the performance of the proposed sAIS approach, we compared the proposed model with the Genetic Algorithm (GA) on the VRP with pickup and delivery benchmark problems, showing average improvements of 7.26%. Conclusions: In this study, we proposed a novel sAIS algorithm for solving VRP with CD problems by simulating human body immune reactions. The experimental results showed that the proposed sAIS algorithm is robustly competitive with the GA on the criterion of average solution quality as measured by the two-sample t-test. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of extracting fundamental learning style stereotypes through the exploitation of the biologically-inspired pattern recognition paradigm of Artificial Immune Systems (AIS). We present an unsupervised computational mechanism which exhibits the ability to reveal the inherent group structure of learning patterns that pervade a given set of educational profiles. We rely on the construction of an Artificial Immune Network (AIN) of learning style exemplars by proposing a correlation-based distance metric. This choice is actually imposed by the categoric nature of the underlying data. Our work utilizes an original dataset which was derived during the conduction of an extended empirical study involving students of the Hellenic Open University. The educational profiles of the students were built by collecting their answers on a thoroughly designed questionnaire taking into account a wide range of personal characteristics and skills. The efficiency of the proposed approach was assessed in terms of cluster compactness. Specifically, we measured the average correlation deviation of the students' education profiles from the corresponding artificial memory antibodies that represent the acquired learning style stereotypes. Finally, the unsupervised learning procedure adopted in this paper was tested against a correlation-based version of the k-means algorithm indicating a significant improvement in performance for the AIS-based clustering approach. [ABSTRACT FROM AUTHOR]

Curve reconstruction from data points is an important issue for advanced medical imaging techniques, such as computer tomography (CT) and magnetic resonance imaging (MRI). The most powerful fitting functions for this purpose are the NURBS (non-uniform rational B-splines). Solving the general reconstruction problem with NURBS requires computing all free variables of the problem (data parameters, breakpoints, control points, and their weights). This leads to a very difficult non-convex, nonlinear, high-dimensional, multimodal, and continuous optimization problem. Previous methods simplify the problem by guessing the values for some variables and computing only the remaining ones. As a result, unavoidable approximations errors are introduced. In this paper, we describe the first method in the literature to solve the full NURBS curve reconstruction problem in all its generality. Our method is based on a combination of two techniques: an immunological approach to perform data parameterization, breakpoint placement, and weight calculation, and least squares minimization to compute the control points. This procedure is repeated iteratively (until no further improvement is achieved) for higher accuracy. The method has been applied to reconstruct some outline curves from MRI brain images with satisfactory results. Comparative work shows that our method outperforms the previous related approaches in the literature for all instances in our benchmark. [ABSTRACT FROM AUTHOR]

Nowadays, the high penetration of automation on smart grids challenges electricity companies in providing an efficient distribution networks operation. In this sense, distribution system reconfiguration (DSR) plays an important role since it may help solve real-time problems. This paper proposes a methodology to solve the DSR problem using artificial immune systems (AIS) based on a new, efficient, and robust approach. This new methodology, called Enhanced Artificial Immune Systems (EAIS), uses the values of the currents in wires for intelligent mutations. The problem is accomplished by a multi-objective optimization with fuzzy variables, minimizing power losses, voltage deviation, and feeders load balancing. A comparison with other DSR solution methods is presented. The method is compared with two other previously proposed methods with the help of the 33-bus, 84-bus, and 136-bus distribution systems. Different scenarios are analyzed, including the optimal location of the Distributed Generation (DG). The results show the applicability of the proposed algorithm for the simultaneous solution of DSR and location or dispatch of DGs. [ABSTRACT FROM AUTHOR]

This paper is an extension of a previous one presented at the conference Cyberworlds 2014. In that work we addressed the problem of obtaining the rational Bézier curve that fits a given set of data points better in the least-squares sense. Our approach was based on the clonal selection theory principles to compute all parameters of the problem, namely, the control points of the approximating curve, their corresponding weights, and a suitable parameterization of data points. Although we were able to obtain results with good accuracy, this scheme can still be significantly improved by hybridizing it with an efficient local search procedure. This is the approach proposed in this paper. In particular, we consider the mesh adaptive search algorithm, a direct search method aimed at improving the local search step to refine the quality of the solution. This hybrid strategy has been applied to six illustrative free-form shapes exhibiting challenging features, including the three examples in previous paper. A comparative analysis of our results with respect to the previous methodology is also reported. Our experimental results show that this hybrid scheme performs extremely well. It also outperforms the previous approach for all instances in our benchmark. [ABSTRACT FROM AUTHOR]

The paper presents the application of the IMGAMO (immune game theory multiobective algorithm) in the optimal design of electrothermal microactuators. Several numerical tests on the mathematical benchmark test functions were performed, showing the superiority of the IMGAMO, when a large number of criteria are considered, in comparison to other multiobjective optimizers. A parametric numerical model of an electrothermal microactuaror was developed and verified. Six functionals, which depend on various thermal and mechanical quantities of the microactuator, were proposed, formulated and numerically implemented. These functionals represent real requirements asked of microactuators. The boundary-value problem of an electro-thermo-mechanical field was solved multiple times during the course of optimization by way of the finite element method (FEM). A numerical example of multiobjective optimization of chevron-type electrothermal actuators is included in the paper. Representation of the multi-dimensional Pareto fronts by means of scatter plot matrices, aided by self-organizing maps (SOMs), is presented. The novel method of selecting interesting, compromise-solutions is proposed and described. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach, i.e., virtual pooling, for optimising returnable transport item (RTI) flows in a two-level closed-loop supply chain. The supply chain comprises a set of suppliers delivering their products loaded on RTIs to a set of customers. RTIs are of various types. The objective is to model a deterministic, multi-supplier, multi-customer inventory routing problem with pickup and delivery of multi-RTI. The model includes inventory-level constraints, the availability of empty RTIs to suppliers, and the minimisation of the total cost, including inventory holding, screening, maintenance, transportation, sharing, and purchasing costs for new RTIs. Furthermore, suppliers with common customers coordinate to virtually pool their inventory of empty RTIs held by customers so that, when loaded RTIs are delivered to customers, each may benefit from this visit to pick up the empty RTI, regardless of the ownership. To handle the combinatorial complexity of the model, a new artificial-immune-system-based algorithm coupled with deep reinforcement learning is proposed. The algorithm combines artificial immune systems' strong global search ability and a strong self-adaptability ability into a goal-driven performance enhanced by deep reinforcement learning, all tailored to the suggested mathematical model. Computational experiments on randomly generated instances highlight the performance of the proposed approach. From a managerial point of view, the results stress that this new approach allows for economies of scale and cost reduction at the level of all involved parties to about 40%. In addition, a sensitivity analysis on the unit cost of transportation and the procurement of new RTIs is conducted, highlighting the benefits and limits of the proposed model compared to dedicated and physical pooling modes. [ABSTRACT FROM AUTHOR]

We propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization, cost minimization and quantum efficiency maximization. We evaluated structures of 15 different cell designs simulated by varying material types and photodiode doping strategies. At first, non-dominated sorting genetic algorithm II (NSGA-II) produced Pareto-optimal-solutions sets for respective cell designs. Then, on investigating quantum efficiencies of all cell designs produced by NSGA-II, we applied a new multi-objective optimization algorithm II (OptIA-II) to discover the Pareto fronts of select (three) best cell designs. Our designed OptIA-II algorithm improved the quantum efficiencies of all select cell designs and reduced their fabrication costs. We observed that the cell design comprising an optimally doped zinc-oxide-based transparent conductive oxide (TCO) layer and rough silver back reflector (BR) offered a quantum efficiency ( Q e ) of 0.6031. Overall, this paper provides a full characterization of cell structure designs. It derives relationship between quantum efficiency, Q e of a cell with its TCO layer's doping methods and TCO and BR layer's material types. Our solar cells design characterization enables us to perform a cost-benefit analysis of solar cells usage in real-world applications. [ABSTRACT FROM AUTHOR]

• 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]

This paper introduces a new ensemble based on different artificial immune algorithms and it is optimized by using a Particle Swarm algorithm. The new proposed architecture of the ensemble introduces a major enhancement to the data classification. The main focus of this paper is devoted for building an ensemble model that integrates three different AIS techniques towards achieving better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome the limitations of the individual algorithms and to achieve synergistic effects through the combination of these techniques. Furthermore, a new method for measuring confidence level of AIS based classifier is introduced in this work as well. On the other hand and in order to enhance the overall performance of the classification process, an optimizer using particle swarm optimization algorithm is going to be adopted. The performance of the proposed ensemble is tested by running several experiments using different medical datasets. [ABSTRACT FROM AUTHOR]

This paper presents two new approaches to solve the reconfiguration problem of electrical distribution systems (EDSs) with variable demands, using the CLONALG and the SGACB algorithms. The CLONALG is a combinatorial optimization technique inspired by biological immune systems, which aims at reproducing the main properties and functions of the system. The SGACB is an optimization algorithm inspired by natural selection and the evolution of species. The reconfiguration problem with variable demands is a complex combinatorial problem that aims at identifying the best radial topology for an EDS, while satisfying all technical constraints at every demand level and minimizing the cost of energy losses in a given operation period. Both algorithms were implemented in C++ and test systems with 33, 84, and 136 nodes, as well as a real system with 417 nodes, in order to validate the proposed methods. The obtained results were compared with results available in the literature in order to verify the efficiency of the proposed approaches. [ABSTRACT FROM AUTHOR]

25 years ago, when AI & Society was launched, the emphasis was, and still is, on dehumanisation and the effects of technology on human life, including reliance on technology. What we forgot to take into account was another very great danger to humans. The pervasiveness of computer technology, without appropriate security safeguards, dehumanises us by allowing criminals to steal not just our money but also our confidential and private data at will. Also, denial-of-service attacks prevent us from accessing the information we need when we want it. We are being dehumanised not by the technology but by criminals who use the ubiquity of the technology and its lack of security to steal from us and prevent us from doing what we want. What is more interesting is that this malevolent use of the technology doesn't come from monolithic corporate structures eager to control our lives but mainly from individuals keen to demonstrate their knowledge of the technology for social networking purposes. The aim of this paper is to turn the clock back 25 years and present an alternative perspective: the single, biggest threat of dehumanisation is not the pervasiveness and ubiquity of computers but the lack of ensuring that humans are provided with the basic security they need for using the technology safely and securely. Cyberspace is not a safe space to be. This was something that even far-sighted researcher colleagues in the 1970s and 1980s overlooked. The paper will explore where we went wrong 25 years ago in our predictions and concerns. We will also present a scenario that allows future generations to have a safer cyberworld. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to use artificial immune systems (AIS) in optimization problems. For this purpose, two major immunological principles presented in the literature are revisited: hypermutation, which is responsible for local search, and receptor edition, used to explore different areas in the solution space. This paper presents three major modifications divided into two different goals. The first goal is to speed up the convergence of each individual. This is done through a new hypermutation approach that uses the numerical information provided by the optimization system to drive the cloning process to interesting directions into the solution space. The second goal regards the reduction of the computational effort necessary to simulate the whole population. This is accomplished by adding to the AIS algorithm two more features of the natural immune system: maturation control and memory cells. The maturation control analyzes the antibodies and, during the convergence process, eliminates possible redundancies, represented by individuals driving to the same local optimum. The last proposed improvement is the use of memory cells in dynamic-optimization scenarios. In such situations, a repertoire of successful cases is used to forecast part of the initial population. Combining these concepts together decreases the number of antibodies, generations, and clones, consequently speeding up the convergence process. Applications illustrate the performance of the proposed method. [ABSTRACT FROM PUBLISHER]

The network theory in immunology inspired the modeling of network based artificial immune system (AIS) models for data clustering. Current network based AIS models determine the network connectivity between artificial lymphocytes (ALCs) by measuring the spatial distance between these ALCs against a distance threshold or by grouping ALCs into sub-networks. This paper discusses alternative network topologies to determine the network connectivity between ALCs and the advantages of using these network topologies. The local network neighborhood AIS model is then proposed as a network based AIS model which uses an index-based ALC neighborhood to determine the network connectivity between ALCs. The proposed model is compared to existing network based AIS models which are applied to data clustering problems. Furthermore, a sensitivity analysis is also done on the proposed model to investigate the influence of the model's parameters on the quality of the clusters. The paper also gives a formal definition of data clustering and discusses the performance measures used to determine the quality of clusters. [ABSTRACT FROM AUTHOR]

Copyright of Transport Problems: an International Scientific Journal is the property of Silesian University of Technology, Faculty of Transport and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The unprecedented scale and number of natural and man-made disasters in the past decade has urged international emergency search and rescue communities to seek for novel technology to enhance operation efficiency. Tele-operated search and rescue robots that can navigate deep into rubble to search for victims and to transfer critical field data back to the control console has gained much interest among emergency response institutions. In response to this need, a low-cost autonomous mini robot equipped with thermal sensor, accelerometer, sonar, pin-hole camera, microphone, ultra-bright LED and wireless communication module is developed to study the control of a group of decentralized mini search and rescue robots. The robot can navigate autonomously between voids to look for living body heat and can send back audio and video information to allow the operator to determine if the found object is a living human. This paper introduces the design and control of a low-cost robotic search and rescue system based on an immuno control framework developed for controlling decentralized systems. Design and development of the physical prototype and the immunity-based control system are described in this paper. [ABSTRACT FROM AUTHOR]

Reduced machining time and increased accuracy for a sculptured surface are both very important when producing complicated parts, so, the step-size and tool-path interval are essential components in high-speed and high-resolution machining. If they are too small, the machining time will increase, whereas if they are too large, rough surfaces will result. In particular, the machining time, which is a key factor in high-speed machining, is affected by the tool-path interval more than the step size. The present paper introduces a ‘system software’ developed to reduce machining time and increased accuracy for a sculptured surface with Non-Uniform Rational B-Spline (NURBS) patches. The system is mainly based on a new and a powerful artificial intelligence (AI) tool, called artificial immune systems ( AIS). It is implemented using C programming language on a PC. It can be used as stand alone system or as the integrated module of a CNC machine tool. With the use of AIS, the impact and power of AI techniques have been reflected on the performance of the tool path optimization system. The methodology of the developed tool path optimization system is illustrated with practical examples in this paper. [ABSTRACT FROM AUTHOR]

Early breast cancer diagnosis is crucial, as it can prevent further complications and save the life of the patient by treating the disease at its most curable stage. In this paper, we propose a new artificial immune system model for associative classification with competitive performance for breast cancer detection. The proposed model has its foundations in the biological immune system; it mimics the detection skills of the immune system to provide correct identification of antigens. TheWilcoxon test was used to identify the statistically significant differences between our proposal and other classification algorithms based on the same bio-inspired model. These statistical tests evidenced the enhanced performance shown by the proposed model by outperforming other immune-based algorithms. The proposed model proved to be competitive with respect to other well-known classification models. In addition, the model benefits from a low computational cost. The success of this model for classification tasks shows that swarm intelligence is useful for this kind of problem, and that it is not limited to optimization tasks. [ABSTRACT FROM AUTHOR]

This review paper attempts to position the area of Artificial Immune Systems (AIS) in a broader context of interdisciplinary research. We review AIS based on an established conceptual framework that encapsulates mathematical and computational modelling of immunology, abstraction and then development of engineered systems. We argue that AIS are much more than engineered systems inspired by the immune system and that there is a great deal for both immunology and engineering to learn from each other through working in an interdisciplinary manner. [ABSTRACT FROM AUTHOR]

The field of Artificial Immune Systems (AIS) concerns the study and development of computationally interesting abstractions of the immune system. This survey tracks the development of AIS since its inception, and then attempts to make an assessment of its usefulness, defined in terms of 'distinctiveness' and 'effectiveness.' In this paper, the standard types of AIS are examined—Negative Selection, Clonal Selection and Immune Networks—as well as a new breed of AIS, based on the immunological 'danger theory.' The paper concludes that all types of AIS largely satisfy the criteria outlined for being useful, but only two types of AIS satisfy both criteria with any certainty. [ABSTRACT FROM AUTHOR]

Early detection of threats to electrical energy distribution systems helps professionals make decisions and mitigate interruptions in supply and improper activation of the protection system. Biologically inspired methods, e.g., artificial neural networks, genetic algorithms, and ant colonies, solve optimization problems and facilitate pattern recognition and decision-making. The present work presents a tool for detecting and classifying voltage disturbances based on the negative selection algorithm, which identifies and eliminates self-reactive cells, associated with multiresolution analysis, which analyzes the signal at different scales of detail, allowing a more complete understanding and detailed description of the phenomenon in question. The negative wavelet selection algorithm demonstrates robustness to detect and classify disturbances. [ABSTRACT FROM AUTHOR]

In order to optimize their assets, electrical power distribution companies seek out various techniques to improve system operation and its different variables, like voltage levels, active power losses and so on. A few of the tools applied to meet these objectives include reactive power compensation, use of voltage regulators, and network reconfiguration. One target most companies aim at is power loss minimization; one available tool to do this is distribution system reconfiguration. To reconfigure a network in radial power distribution systems means to alter the topology changing the state of a set of switches normally closed (NC) and normally opened (NO). In restructured electrical power business, a company must also consider obtaining a topology as reliable as possible. In most cases, reducing the power losses is no guarantee of improved reliability. This paper presents a multiobjective algorithm to reduce power losses while improving the reliability index using the artificial immune systems technique applying graph theory considerations to improve computational performance and Pareto dominance rules. The proposed algorithm is tested on a sample system, 14-bus test system, and on Administración Nacional de Electricidad (ANDE) real feeder (CBO-01 23-kV feeder). [ABSTRACT FROM PUBLISHER]

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]

This paper surveys the major works related to an artificial immune system based classifier that was proposed in the 2000s, namely, the artificial immune recognition system (AIRS) algorithm. This survey has revealed that most works on AIRS was dedicated to the application of the algorithm to real-world problems rather than to theoretical developments of the algorithm. Based on this finding, we propose an improved version of the AIRS algorithm which we dub AIRS3. AIRS3 takes into account an important parameter that was ignored by the original algorithm, namely, the number of training antigens represented by each memory cell at the end of learning ( numRepAg). Experiments of the new AIRS3 algorithm on data sets taken from the UCI machine learning repository have shown that taking into account the numRepAg information enhances the classification accuracy of AIRS. [ABSTRACT FROM AUTHOR]

In this paper an INtegrated Innate and Adaptive Immune System (INIAIS) architecture is described. The architecture is shown to incorporate many properties of natural immune systems, including diversity, self-organization, dynamic adaptation, multi-layered structure, lightweight and disposable components as well as distributed computation. INI AIS is a general artificial immune system architecture that could, in principle, be applied to several domains. In this paper, INIAIS is applied to computer security domain in the form of a network intrusion detection system called INIAIS-NIDS. INIAIS-NIDS is described and shown to; be effective at detecting intrusions, be capable of some automated intrusion response, have increased robustness in intrusions detection, provide improved efficiency of the detection process as a whole, and able of maintaining low false positive rates. [ABSTRACT FROM AUTHOR]

In this paper, a comprehensive survey of evolutionary computation (EC) methods for cybersecurity of mobile ad hoc networks (MANETs) is presented. Typically, EC methods are named based on the natural processes inspiring them, such as swarm intelligence (e.g., ant colony optimization, artificial bee colony, and particle swarm optimization), evolutionary algorithms (e.g., genetic algorithms, genetic programming, grammatical evolution, and differential evolution), artificial immune systems, and evolutionary games analyzing strategic interactions among different population types. We introduce these methods with their typical applications, and commonly used algorithms to improve cybersecurity within the scope of MANETs. Ongoing and speedy topology changes, multi-hop communication, non-hierarchical organization, and power and computational limitations are among the intrinsic characteristics of MANETs causing cybersecurity vulnerabilities. We describe basic defense mechanisms in MANETs for vulnerability detection, attack deterrence, prevention and recovery, and risk mitigation. We classify principal applications of EC as intrusion detection, trust management, and cryptography in cybersecurity systems to counter measure adversarial activities. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of recommendation with the context of one-class classification. Specifically, we employ the alternative machine learning framework of Artificial Immune Systems (AIS) in order to develop user-specific preference models. Our approach is based on the fact that users experience a major difficulty in articulating their music preferences while at the same time they are extremely reluctant in providing counter-examples of their music habits. Therefore, developing coherent user models on the grounds of both positive (desirable) and negative (non-desirable) training samples is not a feasible task since the class of non-favorable data patterns is severely under-represented. Our recommendation approach alleviates the need to collect negative feedback form the user by building recommendation models that exclusively rely on the presence of a limited number positive data items. Such models are built, however, by trying to efficiently cover the complementary space of non-desirable patterns. This is achieved through the utilization of V-Detector, an AIS-based one-class classification algorithm, which operates by developing a set of variable-sized detectors for the subspace of non-preferable music items. V-Detector, despite being exclusively fed with instances from the positive class, focuses on delivering an accurate model of the negative space. Based on this complementarity, our recommendation algorithm is able to implicitly model individual user preferences that span arbitrary-shaped and fragmented regions of the complete space of patterns. The proposed recommendation approach was experimentally evaluated in terms of its efficiency to correctly identify the user-defined classes of positive and negative preference. The obtained results justify its superiority against traditional one-class classification approaches. [ABSTRACT FROM AUTHOR]

Determining the size and quality of transport needs would not be possible without adequate forecasting based on the sales volume or demand for this service from the past periods. Traditional forecasting methods use econometric models that may be subject to serious errors. The use of the methods taking into account the variability of the studied phenomena or more advanced mathematical methods enables to minimize the error. Various methods of artificial intelligence such as a neural network, fuzzy sets, genetic algorithms, etc., have been recently successfully applied. The aim of this paper is to compare three forecasting methods that can be used for predicting the volume of road freight. The article deals with the effectiveness of three prediction methods, namely Winters method for seasonal problems - a multiplicative version, harmonic analysis and harmonic analysis aided by the artificial immune system. The effectiveness of prediction was counted using MAPE errors (main average percentage error). The results of calculations were compared and the best example was presented. [ABSTRACT FROM AUTHOR]

The data classification task is one of the main tasks within the knowledge discovering from databases field. Its goal is to allow the correct classification of new objects (records from a database), unknown to the classifier, based upon the extraction of knowledge from objects whose classes are known a priori. The known data can be used to generate a classification model, or simply to infer the class of new objects from those whose classes are known. This paper presents a proposal for a classification algorithm, called Constructive Particle Swarm Classifier (cPSClass), which uses mechanisms from the Particles Swarm Clustering algorithm and Artificial Immune Systems to determine dynamically the number of prototypes from a database and use them to predict the correct class to which a new input object should belong. For performance evaluation the cPSClass was applied to several datasets from the literature and its performance was compared with that of its predecessor version, the nonconstructive Particle Swarm Classifier, and also to some classic algorithms from the literature. [ABSTRACT FROM AUTHOR]

Recently, clonal selection theory in the immune system has received the attention of researchers and given them inspiration to create algorithms that evolve candidate solutions by means of selection, cloning, and mutation procedures. Moreover, diversity in the population is enabled by means of the receptor editing process. The Clonal Selection Algorithm (CSA) in its canonical form and its various versions are used to solve different types of problems and are reported to perform better compared with other heuristics (i.e., genetic algorithms, neural networks, etc.) in some cases, such as function optimization and pattern recognition. Although the studies related with CSA are increasingly popular, according to our best knowledge, there is no study summarizing the basic features of these algorithms, hybrid algorithms, and the application areas of these algorithms all in one paper. Therefore, this study aims to summarize the powerful characteristics and general review of CSA. In addition, CSA based hybrid algorithms are reviewed, and open research areas are discussed for further research. [ABSTRACT FROM AUTHOR]

Many recent advances have been made in understanding the functional implications of the global topological properties of biological networks through the application of complex network theory, particularly in the area of small-world and scale-free topologies. Computational studies which attempt to understand the structure–function relationship usually proceed by defining a representation of cells and an affinity measure to describe their interactions. We show that this necessarily restricts the topology of the networks that can arise—furthermore, we show that although simple topologies can be produced via representation and affinity measures common in the literature, it is unclear how to select measures which result in complex topologies, for example, exhibiting scale-free functionality. In this paper, we introduce the concept of the potential network as a method in which abstract network topologies can be directly studied, bypassing any definition of shape-space and affinity function. We illustrate the benefit of the approach by studying the evolution of idiotypic networks on a selection of scale-free and regular topologies, finding that a key immunological property—tolerance—is promoted by bi-partite and heterogeneous topologies. The approach, however, is applicable to the study of any network and thus has implications for both immunology and artificial immune systems. [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate how a Danger Theory based Artificial Immune System-in particular the Dendritic Cell Algorithm (DCA) can detect an attack on a sensor network. The method is validated using two separate implementations: a simulation using J-sim and an implementation for the T-mote Sky sensor using TinyOS. This paper also introduces a new sensor network attack called an Interest Cache Poisoning Attack and investigates how the DCA can be applied to detect this attack in a series of experiments. [ABSTRACT FROM AUTHOR]

In this paper, we introduce Lamarckian learning theory into the Clonal Selection Algorithm and propose a sort of Lamarckian Clonal Selection Algorithm, termed as LCSA. The major aim is to utilize effectively the information of each individual to reinforce the exploitation with the help of Lamarckian local search. Recombination operator and tournament selection operator are incorporated into LCSA to further enhance the ability of global exploration. We compare LCSA with the Clonal Selection Algorithm in solving twenty benchmark problems to evaluate the performance of LCSA. The results demonstrate that the Lamarckian local search makes LCSA more effective and efficient in solving numerical optimization problems. [ABSTRACT FROM AUTHOR]

Clonal selection feature selection algorithm (CSFS) based on clonal selection algorithm (CSA), a new computational intelligence approach, has been proposed to perform the task of dimensionality reduction in high-dimensional images, and has better performance than traditional feature selection algorithms with more computational costs. In this paper, a fast clonal selection feature selection algorithm (FCSFS) for hyperspectral imagery is proposed to improve the convergence rate by using Cauchy mutation instead of non-uniform mutation as the primary immune operator. Two experiments are performed to evaluate the performance of the proposed algorithm in comparison with CSFS using hyperspectral remote sensing imagery acquired by the pushbroom hyperspectral imager (PHI) and the airborne visible/infrared imaging spectrometer (AVIRIS), respectively. Experimental results demonstrate that the FCSFS converges faster than CSFS, hence providing an effective new option for dimensionality reduction of hyperspectral remote sensing imagery. [ABSTRACT FROM AUTHOR]

The successful handling of numerous real–world complex problems has increased the popularity of nature–inspired intelligent (NII) algorithms and techniques. Their successful implementation primarily on difficult and complicated optimization problems, stresses their upcoming importance in the broader area of artificial intelligence. NII techniques take advantage of the way that biological systems deal with real–world situations. Specifically, they simulate the way real biological systems, such as the human brain, ant colonies and human immune system work, when solving complex real–world situations. In this survey paper, we briefly present a number of selected NII approaches and we point particular suitable areas of application for each of them. Specifically, five major categories of nature inspired approaches are presented, namely, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), DNA computing, artificial immune systems and membrane computing. Applications include problems related to optimization (financial, industrial and medical), task scheduling, system design (optimization of the system's parameters), image processing and data processing (feature selection and classification). We also refer to collaboration between NII techniques and classical AI methodologies, such as neural networks, genetic algorithms, fuzzy logic, etc. The current survey states that NII techniques are likely to become the next step in the rapid evolution of artificial intelligence tools. [ABSTRACT FROM AUTHOR]

Previous work has shown that immune-inspired techniques have good potential for solving problems associated with the development of real-time embedded systems (RTES), where for various reasons traditional real-time development techniques are not suitable. This paper examines in more detail the general applicability of the Dendritic Cell Algorithm (DCA) to the problem of task scheduling in RTES. To make this possible, an understanding of the problem characteristics is formalised, such that the results produced by the DCA can be examined in relation to the overall problem difficulty. The paper then contains a detailed understanding of how well the DCA which demonstrates that it generally performs well, however it clearly identifies properties of anomalies that are difficult to detect. These properties are as anticipated based on real-time scheduling theory. [ABSTRACT FROM AUTHOR]

This paper presents an immune-inspired adaptable error detection (AED) framework for automated teller machines (ATMs). This framework has two levels: one is local to a single ATM, while the other is network-wide. The framework employs vaccination and adaptability analogies of the immune system. For discriminating between normal and erroneous states, an immune-inspired one-class supervised algorithm was employed, which supports continual learning and adaptation. The effectiveness of the proposed approach was confirmed in terms of classification performance and impact on availability. The overall results are encouraging as the downtime of ATMs can de reduced by anticipating the occurrence of failures before they actually occur. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of electric distribution network expansion under condition of uncertainty in the evolution of node loads in a time horizon. An immune-based evolutionary optimization algorithm is developed here, in order to find not only the optimal network, but also a set of suboptimal ones, for a given most probable scenario. A Monte-Carlo simulation of the future load conditions is performed, evaluating each such solution within a set of other possible scenarios. A dominance analysis is then performed in order to compare the candidate solutions, considering the objectives of: smaller infeasibility rate, smaller nominal cost, smaller mean cost and smaller fault cost. The design outcome is a network that has a satisfactory behavior under the considered scenarios. Simulation results show that the proposed approach leads to resulting networks that can be rather different from the networks that would be found via a conventional design procedure: reaching more robust performances under load evolution uncertainties. [ABSTRACT FROM AUTHOR]

Some optimization algorithms based on theories from immunology have the feature of finding an arbitrary number of optima, including the global solution. However, this advantage comes at the cost of a large number of objective function evaluations, in most cases, prohibitive in electromagnetic design. This paper proposes a modified version of the artificial immune network algorithm (opt-AlNet) for electromagnetic design optimization. The objective of this modified AlNet (m-AINet) is to reduce the computational effort required by the algorithm, while keeping or improving the convergence characteristics. Another improvement proposed is to make it more suitable for constrained problems through the utilization of a specific constraint-handling technique. The results obtained over an analytical problem and the design of an electromagnetic device show the applicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Understanding which function classes are easy and which are hard for a given algorithm is a fundamental question for the analysis and design of bio-inspired search heuristics. A natural starting point is to consider the easiest and hardest functions for an algorithm. For the (1+1) EA using standard bit mutation (SBM) it is well known that OneMax is an easiest function with unique optimum while Trap is a hardest. In this paper we extend the analysis of easiest function classes to the contiguous somatic hypermutation (CHM) operator used in artificial immune systems. We define a function MinBlocks and prove that it is an easiest function for the (1+1) EA using CHM, presenting both a runtime and a fixed budget analysis. Since MinBlocks is, up to a factor of 2, a hardest function for standard bit mutations, we consider the effects of combining both operators into a hybrid algorithm. We rigorously prove that by combining the advantages of k operators, several hybrid algorithmic schemes have optimal asymptotic performance on the easiest functions for each individual operator. In particular, the hybrid algorithms using CHM and SBM have optimal asymptotic performance on both OneMax and MinBlocks. We then investigate easiest functions for hybrid schemes and show that an easiest function for a hybrid algorithm is not just a trivial weighted combination of the respective easiest functions for each operator. [ABSTRACT FROM AUTHOR]

Pattern classification is a central problem in machine learning, with a wide array of applications, and rule-based classifiers are one of the most prominent approaches. Among these classifiers, Incremental Rule Learning algorithms combine the advantages of classic Pittsburg and Michigan approaches, while, on the other hand, classifiers using fuzzy membership functions often result in systems with fewer rules and better generalization ability. To discover an optimal set of rules, learning classifier systems have always relied on bio-inspired models, mainly genetic algorithms. In this paper we propose a classification algorithm based on an efficient bio-inspired approach, Artificial Immune Networks. The proposed algorithm encodes the patterns as antigens, and evolves a set of antibodies, representing fuzzy classification rules of ellipsoidal surface, to cover the problem space. The innate immune mechanisms of affinity maturation and diversity preservation are modified and adapted to the classification context, resulting in a classifier that combines the advantages of both incremental rule learning and fuzzy classifier systems. The algorithm is compared to a number of state-of-the-art rule-based classifiers, as well as Support Vector Machines (SVM), producing very satisfying results, particularly in problems with large number of attributes and classes. [ABSTRACT FROM AUTHOR]