Application areas of AIS: The past, the present and the future

An improved immune algorithm with parallel mutation and its application

The objective of this paper is to design a fast and efficient immune algorithm for solving various optimization problems. The immune algorithm (IA), which simulates the principle of the biological immune system, is one of the nature-inspired algorithms and its many advantages have been revealed. Although IA has shown its superiority over the traditional algorithms in many fields, it still suffers from the drawbacks of slow convergence and local minima trapping problems due to its inherent stochastic search property. Many efforts have been done to improve the search performance of immune algorithms, such as adaptive parameter setting and population diversity maintenance. In this paper, an improved immune algorithm (IIA) which utilizes a parallel mutation mechanism (PM) is proposed to solve the Lennard-Jones potential problem (LJPP). In IIA, three distinct mutation operators involving cauchy mutation (CM), gaussian mutation (GM) and lateral mutation (LM) are conditionally selected to be implemented. It is expected that IIA can effectively balance the exploration and exploitation of the search and thus speed up the convergence. To illustrate its validity, IIA is tested on a two-dimension function and some benchmark functions. Then IIA is applied to solve the LJPP to exhibit its applicability to the real-world problems. Experimental results demonstrate the effectiveness of IIA in terms of the convergence speed and the solution quality.

A Review on Immune-Inspired Node Fault Detection in Wireless Sensor Networks with a Focus on the Danger Theory

The use of fault detection and tolerance measures in wireless sensor networks is inevitable to ensure the reliability of the data sources. In this context, immune-inspired concepts offer suitable characteristics for developing lightweight fault detection systems, and previous works have shown promising results. In this article, we provide a literature review of immune-inspired fault detection approaches in sensor networks proposed in the last two decades. We discuss the unique properties of the human immune system and how the found approaches exploit them. With the information from the literature review extended with the findings of our previous works, we discuss the limitations of current approaches and consequent future research directions. We have found that immune-inspired techniques are well suited for lightweight fault detection, but there are still open questions concerning the effective and efficient use of those in sensor networks.

Immunological Approach for Full NURBS Reconstruction of Outline Curves from Noisy Data Points in Medical Imaging

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.

Towards the development of robot immune system: A combined approach involving innate immune cells and T-lymphocytes

Mobile robots in uncertain and unstructuredenvironments frequently encounter faults. Therefore, an effective fault detection and recovery mechanism is required. One can possibly investigate natural systems to seek inspiration to develop systems that can handle such faults. Authors, in this pursuit, have explored the possibility of designing an artificial immune system, called Robot Immune System (RIS), to maintain a robot's internal health-equilibrium. This contrasts with existing approaches in which specific robotic tasks are performed instead of developing a self-healing robot. In this respect, a fault detection and recovery methodology based on innate and adaptive immune functions has been successfully designed and developed. The immuno-inspired methodology is applied to a simulated robot using Robot Operating System and Virtual Robot Experimentation Platform. Through extensive simulations in increasingly difficult scenarios, the RIS has proven successful in autonomously detecting the abnormal behaviors, performing the recovery actions, and maintaining the homeostasis in the robot. In addition to being multi-tiered, the developed RIS is also a non-deterministic and population-based system.

Artificial Immune Optimization Algorithm

Artificial immune system (AIS) is a paradigm inspired by processes and metaphors of natural immune system (NIS). There is a rapidly growing interest in AIS approaches to machine learning and especially in the domain of optimization. Of particular interest is the way human body responds to diseases and pathogens as well as adapts to remain immune for long periods after a disease has been combated. In this chapter, we are presenting a novel multilayered natural immune system (NIS) inspired algorithms in the domain of optimization. The proposed algorithm uses natural immune system components such as B-cells, Memory cells and Antibodies; and processes such as negative clonal selection and affinity maturation to find multiple local optimum points. Another benefit this algorithm presents is the presence of immunological memory that is in the form of specific memory cells which keep track of previously explored solutions. The algorithm is evaluated on two well-known numeric functions to demonstrate the applicability.

Theoretical formulation and analysis of the deterministic dendritic cell algorithm

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 O(n(2)) 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 O(max(nN,nz)), 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.

Appropriate degree of trust: deriving confidence metrics for automatic peak assignment in high-resolution mass spectrometry

Techniques for deriving confidence metrics for the reliability of automatically assigned elemental formulas in complex spectra, from high-resolution mass spectrometers, are described. These metrics can help an analyst to place an appropriate degree of trust in the results obtained from automated spectral analysis of, for example, natural organic materials. To provide these metrics of confidence, common mass spectrometric tests for reliability of peak assignment (mass accuracy/error, relative ion abundance, and rings-plus-double-bonds equivalence) are combined with novel confidence metrics based on the interconnectivity and consistency of a mass difference or mass defect based peak inference network and on the confidence of the initial library matches. These are shown to provide improved peak assignment confidence over manual or simple automatic assignment methods.

Use of an artificial immune system derived method for the charge state assignment of small-molecule mass spectra

Knowing the charge state of an ion in a mass spectrum is crucial to being able to assign a formula to it. For many small-molecule peaks in complex mass spectra, the intensities of the isotopic peaks are too low to allow the charge state to be calculated from isotopic spacings, which is the basis of the conventional method of determining the charge state of an ion. A novel artificial intelligence derived method for identifying the charge state of ions, in the absence of any isotopic information or a series of charge states, has been developed using an artificial immune system approach. This technique has been tested against synthetic and real data sets and has proven successful in identifying the majority of multiply charged ions, thereby significantly improving the peak assignment rate and confidence.