WCCI 2006

AIS Session


Title: The Link Between r-contiguous Detectors and k-CNF Satisfiability

Author: Thomas Stibor, Jonathan Timmis, Claudia Eckert

Abstract: In the context of generating detectors using the r-contiguous matching rule, questions have been raised at the effciency of the process. We show that the problem of generating r-contiguous detectors can be transformed in a k-CNF satisfiability problem. This insight allows for the wider understanding of the problem of generating r-contiguous detectors. Moreover, we apply this result to consider questions relating to the complexity of generating detectors, and when detectors are generable.


Title: libtissue - implementing innate immunity

Author: Jamie Twycross, Uwe Aickelin

Abstract: In a previous paper the authors argued the case for incorporating ideas from innate immunity into artificial immune systems (AISs) and presented an outline for a conceptual framework for such systems. A number of key general properties observed in the biological innate and adaptive immune systems were highlighted, and how such properties might be instantiated in artificial systems was discussed in detail. The next logical step is to take these ideas and build a software system with which AISs with these properties can be implemented and experimentally evaluated. This paper reports on the results of that step - the libtissue system.


Title: On the Investigation of Artificial Immune Systems on Imbalanced Data Classification for Power Distribution System Fault Cause Identification

Author: Le Xu, Mo-Yuen Chow, Jon Timmis, Leroy S. Taylor and Andrew Watkins

Abstract: Imbalanced data are often encountered in realworld applications, they may incline the performance of classification to be biased. The immune-based algorithm Artificial Immune Recognition System (AIRS) is applied to Duke Energy distribution systems outage data and we investigate its capability to classify imbalanced data. The performance of AIRS is compared with an Artificial Neural Network (ANN). Two major distribution fault causes, tree and lightning strike, are used as prototypes and a tailor-made measure for imbalanced data, g-mean, is used as the major performance measure. The results indicate that AIRS is able to achieve a more balanced performance on imbalanced data than ANN.


Title: Data Mining based on Gene Expression Programming and Clonal Selection

Author: Vassilios K. Karakasis and Andreas Stafylopatis

Abstract: A hybrid evolutionary technique is proposed for data mining tasks, which combines the Clonal Selection Principle with Gene Expression Programming (GEP). The proposed algorithm introduces the notion of Data Class Antigens, which is used to represent a class of data. The produced rules are evolved by a clonal selection algorithm, which extends the recently proposed CLONALG algorithm. In the present algorithm, among other new features, a receptor editing step has been incorporated. Moreover, the rules themselves are represented as antibodies, which are coded as GEP chromosomes, in order to exploit the flexibility and the expressiveness of such encoding. The algorithm is tested on some benchmark problems of the UCI repository, and in particular on the set of MONK problems and the Pima Indians Diabetes problem. In both problems, the results in terms of prediction accuracy are very satisfactory, albeit slightly less accurate than those obtained by a standard GEP technique. In terms of convergence rate and computational efficiency, however, the technique proposed here markedly outperforms the standard GEP algorithm.


Title: Analysis of Dental Images using Artificial Immune Systems

Author: Zhou Ji, Dipankar Dasgupta, Zhiling Yang and Hongmei Teng

Abstract: This paper introduces a preliminary effort to develop an automatic image analysis method using Artificial Immune Systems for clinical dental diagnosis. To diagnose dental deformity, especially malocclusion, manual measurement of certain geometry on the X-ray images is traditionally used, which relies on subjective judgment to determine the reference points. This paper proposes a feature extraction method that is based on the brightness distribution of the image instead of the anatomical parts. A negative selection algorithm is then applied to the data represented as real-valued vectors to detect the cases of severe malocclusion. Using the same data representation, oneclass SVM was also tried to compare the detection capability with the negative selection algorithm. The results show that the negative selection algorithm appears more suitable for this problem.


Title: IIDLE: An Immunological Inspired Distributed Learning Environment for Multiple Objective and Hybrid Optimisation

Author: Jason Brownlee

Abstract: The acquired immune system is a robust and powerful information processing system that demonstrates features such as decentralised control, parallel processing, adaptation, and learning. The Immunological Inspired Distributed Learning Environment (IIDLE) is a clonal selection inspired Artificial Immune System (AIS) that exploits the inherent parallelism, decentralised control, spatially distributed nature, and learning behaviours of the immune system. The distributed architecture and modular process of the IIDLE framework are shown to be useful features on complex search and optimisation tasks in addition to facilitating some of the desired robustness of the inspiration.

Poster Session


Title: Dendritic Cells for Anomaly Detection

Author: Julie Greensmith, Jamie Twycross and Uwe Aickelin

Abstract: Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop an intrusion detection system based on a novel concept in immunology, the Danger Theory. Dendritic Cells (DCs) are antigen presenting cells and key to the activation of the human immune system. DCs perform the vital role of combining signals from the host tissue and correlate these signals with proteins known as antigens. In algorithmic terms, individual DCs perform multi-sensor data fusion based on time-windows. The whole population of DCs asynchronously correlates the fused signals with a secondary data stream. The behaviour of human DCs is abstracted to form the DC Algorithm (DCA), which is implemented using an immune inspired framework, libtissue. This system is used to detect context switching for a basic machine learning dataset and to detect outgoing portscans in real-time. Experimental results show a significant difference between an outgoing portscan and normal traffic.


Title: New Perspectives for the Biclustering Problem

Author: Fabricio O. de Franca, George Bezerra and Fernando Von Zuben

Abstract: Multimodal optimization algorithms inspired by the immune system are generally characterized by a dynamic control of the population size and by diversity maintenance along the search. One of these proposals, denoted copt-aiNet (artificial immune network for combinatorial optimization), is used to deal with combinatorial problems like the Traveling Salesman Problem (TSP) and other permutation problems. In this paper, the copt-aiNet algorithm is extended and adapted to be applied to an important issue of modern data mining, the biclustering problem. The biclustering approach consists in simultaneously ordering the rows and columns of a given matrix, so that similar elements are grouped together. To illustrate the performance of the proposed method, two bitmap images are scrambled and used as input to the algorithm, and the biclustering procedure tries to restore the original image by grouping the pixels according to the similarity of colors in a neighborhood. Additionally, copt-aiNet is applied to gene expression data clustering, a classical problem of the bioinformatics literature, and its performance is compared with a hierarchical biclustering algorithm.


Title: The Time Adaptive Self-Organizing Map Is a Neural Network Based on Artificial Immune System

Author: Hamed Shah-Hosseini

Abstract: In this paper, the similarities between the mechanisms used in the TASOM (Time Adaptive Self- Organizing Map) neural network and AIS (Artificial Immune Systems) are analyzed. To demonstrate the similarities, AIS mechanisms are incorporated into the TASOM network such as the weight updating is replaced by a mutation mechanism. Learning rate and neighborhood sizes are also replaced by the clonal selection process used in AIS. This new network is called TAISOM. Experimental results with TAISOM are implemented for uniform and Gaussian distributions for one and two-dimensional lattices of neurons. These experiments show that TAISOM learns its environment as expected so that neurons fill the environments quite well and the neurons also preserve the topological ordering.


Title: An Immunological Density-Preserving Approach to the Synthesis of RBF Neural Networks for Classification

Author: Tiago Barra, George Bezerra, Leandro de Castro and Fernando Von Zuben

Abstract: Radial Basis Function (RBF) neural networks are universal approximators and have been used for a wide range of applications. Aiming at reducing the number of neurons in the hidden layer, for regularization purposes, the center and dispersion of each RBF have to be properly defined by means of competitive learning. Only the output weights will be defined in a supervised manner. One of the drawbacks of such learning methodology, involving unsupervised and supervised learning, is that the centers will be defined so that regions in the input space with a high density of samples tend to be under-represented and those regions with a low density of samples tend to be overrepresented. Additionally, few approaches provide a proper and individual indication of the dispersion of each RBF. This paper presents an immune density-preserving algorithm with adaptive radius, called ARIA, to determine the number of centers, their location and the dispersion of each RBF, based only on the available training data set. Considering classification problems, the algorithm to determine the hidden layer is compared to another immune-inspired algorithm called aiNet, K-means and the random choice of centers. The classification accuracy of the final network is compared to another density based approach and a decision tree classifier, C 5.0. The results are reported and analyzed.

Other Sessions


Title: Biologically Inspired Evolutionary Agent Systems in Dynamic Environments

Author: Ki-Won Yeom and Ji-Hyung Park

Abstract: We introduce a new bio-inspired agent system based on vertebrate immune system and describe the evolutionary metaphor for adaptation to dynamic environments using genetic algorithm. Biological information processing systems have various interesting characteristics from an engineering viewpoint. Among them, the immune system plays an important role in maintaining its own system against dynamically changing environments. Based on this fact, we have investigated a new decentralized consensus-making system for the behavior of autonomous mobile robots, inspired by the immune idiotypic network hypothesis in immunology. The developmental encoding scheme is used to translate a given genotype into a complete agent, which then acts in a physically-realistic virtual environment. Evolution is accomplished using a genetic algorithm, in which the genotypes are treated as genetic regulatory networks. The dynamics of the regulatory network direct the growth of the agent, and lead to the construction of both the morphology and neural control of the agent. We demonstrate that such a model can be used to evolve agents to perform non-trivial tasks, such as directed locomotion and block pushing in a noisy environment.


Title: An Immune-based Multilayered Cognitive Model for Autonomous Navigation

Author: Diego A. Romero, Fernando Nino

Abstract: In this work, an immune-based multilayer cognitive model for autonomous navigation is proposed. Each layer is modeled by a bioinspired technique, namely, neural networks and artificial immune systems. In this research, a new immune based algorithm is proposed, which is a combination of an algorithm based on immune network theory and a reinforcement learning technique. The proposed approach was tested on several environments and it exhibited interesting learning capabilities in solving an autonomous navigation problem.


Title: An Immune-based Algorithm for Topology Optimization

Author: Felipe Campelo, Frederico G. Guimaraes, Hajime Igarashi, Kota Watanabe, and Jaime A. Ramirez

Abstract: Traditional shape optimization of engineering devices usually starts with an initial user-defined configuration of material. Optimization algorithms are then applied for optimizing objective functions of predefined parameters. While this approach can yield efficient results, it is essentially limited, since limitations in the initial design forbid the computational methods to explore different distributions of material as solutions for a given problem. In other words, the algorithms are not allowed to exhibit creativity in the design process. Topology optimization is a paradigm for optimization that allows such creativity to emerge. Instead of optimizing functions of user-defined parameters, this paradigm optimizes the material properties of each point of the design space, and its methods are theoretically able to describe all possible devices within a limited space. This work presents a new methodology for topology optimization, based on an evolutionary paradigm known as artificial immune systems. The proposed technique is capable of exploring the space locally as well as globally, efficiently searching for the optimal distribution of material. It also incorporates strategies for the evolution of smoother, more regular shapes, in order to generate physically feasible solutions for engineering problems.


Title: The Influence Of The Pool Of Candidates On The Performance Of Selection And Combination Techniques In Ensembles

Author: Guilherme P. Coelho and Fernando J. Von Zuben

Abstract: In this paper, we propose the use of an immuneinspired approach called opt-aiNet to generate a diverse set of high-performance candidates to compose an ensemble of neural network classifiers. Being a population-based search algorithm, the opt-aiNet is capable of maintaining diversity and finding many high-performance solutions simultaneously, which are known to be desired features when synthesizing an ensemble. Concerning the selection and combination phases, the most relevant selection and combination techniques already proposed in the literature have been considered. The main contribution of this paper is the indication that there is no pair of selection/combination technique that can be considered the best one, because the performance of the obtained ensemble varies significantly with the current composition of the pool of candidates already produced by the generation phase. Notwithstanding, this variability in performance is not restricted to the choice of opt-aiNet as the generative device. As a consequence, to overcome the performance of the best individual classifier, every possible pairs of selection and combination techniques should be tried. Only with such an exhaustive search (notice that the main computational burden is usually related to the generation phase), the performance of the ensemble was invariably superior to the performance of the best individual classifier on four benchmark classification problems.


Title: Immune Algorithm Based Routing Optimization in Fourth-Party Logistics

Author: Min Huang, Wei Tong, Qing Wang, Xin Xu and Xingwei Wang

Abstract: Recently, Fourth-Party Logistics (4PL) is receiving considerable attention in the manufacturing and retail industries. However, due to the complexity, the research of routing problem in 4PL is in an initial stage. The existing study does not consider the complicated problem with node-edge property. This paper studies the node-to-node routing problem in 4PL. A mathematical model is set up based on nonlinear integer programming and multi-graph. With respect to the problem’s characteristics a mechanism for simplification is designed. To solve the problem model a hybrid algorithm is designed, in which Dijkstra algorithm is embedded. The simulation shows that the hybrid algorithm embedded with Dijkstra algorithm is effective.


Title: An Immune Fault Detection System for Analog Circuits with Automatic Detector Generation

Author: Jorge Amaral, Jose Amaral and Ricardo Tanscheit

Abstract: This work focuses on fault detection of electronic analog circuits. A fault detection system for analog circuits based on cross-correlation and artificial immune systems is proposed. It is capable of detecting faulty components in analog circuits by analyzing its impulse response. The use of crosscorrelation for preprocessing the impulse response drastically reduces the size of the detector used by the Real-valued Negative Selection Algorithm (RNSA). The proposed method can automatically generate very efficient detectors by using quadtree decomposition. Results have demonstrated that the proposed system is able to detect faults in a Sallen-Key bandpass filter and in a continuous-time state variable filter.


Title: Bayesian Learning of Neural Networks by Means of Artificial Immune Systems

Author: Pablo A. D. Castro and Fernando J. Von Zuben

Abstract: Once the design of Artificial Neural Networks (ANN) may require the optimization of numerical and structural parameters, bio-inspired algorithms have been successfully applied to accomplish this task, since they are population-based search strategies capable of dealing successfully with complex and large search spaces, avoiding local minima. In this paper, we propose the use of an Artificial Immune System for learning feedforward ANN's topologies. Besides the number of neurons in the hidden layer, the algorithm also optimizes the type of activation function for each node. The use of a Bayesian framework to infer the weights and weight decay terms as well as to perform model selection allows us to find neural models with high generalization capability and low complexity, once the Occam’s razor principle is incorporated into the framework. We demonstrate the applicability of the proposal on seven classification problems and promising results were obtained.


Title: Immune-inspired Dynamic Optimization for Blind Spatial Equalization in Undermodeled Channels

Author: Cynthia Junqueira, Fabricio O. de Franca, Romis R. F. Attux, Cristiano M. Panazio and Leandro N. de Castro

Abstract: In this work, we propose an evolutionary-like approach to the problem of blind adaptive spatial filtering that is based on the decision-directed criterion and on the doptaiNet, an artificial immune network conceived to perform multimodal search in dynamic environments. The proposal was tested under static and time-varying undermodeled channel models, and, in all cases, its ability to find and track a solution close to the Wiener global optimum was attested. The obtained results reveal that the dopt-aiNet may decisively enhance the performance of adaptive arrays in scenarios built from elements that are representative of some aspects of real-world communication systems.


Title: Handling Time-Varying TSP Instances

Author: Fabricio O. de Franca, Lalinka Gomes, Leandro de Castro and Fernando Von Zuben

Abstract: Multimodal optimization algorithms are being adapted to deal with dynamic optimization, mainly due to their ability to provide a faster reaction to unexpected changes in the optimization surface. The faster reaction may be associated with the existence of two important attributes in populationbased algorithms devoted to multimodal optimization: simultaneous maintenance of multiple local optima in the population; and self-regulation of the population size along the search. The optimization surface may be subject to variations motivated by one of two main reasons: modification of the objectives to be fulfilled and change in parameters of the problem. An immuneinspired algorithm specially designed to deal with combinatorial optimization is applied here to solve time-varying TSP instances, with the cost of going from one city to the other being a function of time. The proposal presents favorable results when compared to the results produced by a high-performance ant colony optimization algorithm of the literature.


Title: An Anomaly Detection-Based Classification System

Author: Haiyu Hou and Gerry Dozier

Abstract: In this paper, we describe the construction of a classification system based on an anomaly detection system that employs constraint-based detectors, which are generated using a genetic algorithm. The performance of the classification system was evaluated using two benchmark datasets including the Wisconsin Breast Cancer Dataset and the Fisher's Iris Dataset.


Title: Immune Learning Classifier Networks: Evolving Nodes and Connections

Author: Renato Reder Cazangi and Fernando Von Zuben

Abstract: The design of an autonomous navigation system with multiple tasks to be accomplished in unknown environments represents a complex undertaking.With the simultaneous purposes of capturing targets and avoiding obstacles, the challenge may become still more intricate if the configuration of obstacles and targets creates local minima, like concave shapes and mazes between the robot and the target. Pure reactive navigation systems are not able to deal properly with such hampering scenarios, requiring additional cognitive apparatus. Concepts from immune network theory are then employed to convert an earlier reactive robot controller, based on learning classifier systems, into a connectionist device. Starting from no a priori knowledge, both the classifiers and their connections are evolved during the robot navigation. Some experiments with and without local minima are carried out and the proposed evolutionary network of classifiers was shown to produce connectionist navigation systems capable of successfully overcoming local minima.


Title: A Neuro-Immune Network for Solving the Traveling Salesman Problem

Author: Rodrigo Pasti and Leandro de Castro

Abstract: Many combinatorial optimization problems belong to the NP class and, thus, cannot be solved optimally in feasible time using standard techniques (e.g., enumeration methods). NP problems have been tackled with some success by techniques known as meta-heuristics. The present paper proposes a new meta-heuristics for solving traveling salesman problems (TSP) based on a neural network trained using ideas from the immune system. The network is self-organized and the learning algorithm aims at locating one network cell at each position of a city of the TSP instance to be solved. The pre-defined network neighborhood is going to establish the final route proposed for the TSP. The algorithm is applied to several instances from the literature and the results compared with the best solutions available.


Title: A Supervised Constructive Neuro-Immune Network for Pattern Classification

Author: Helder Knidel, Fernando Von Zuben and Leandro Nunes de Castro

Abstract: This paper proposes a supervised version of a learning algorithm for a constructive neuro-immune network. The proposed methodology is developed by taking ideas from the immune system and learning vector quantization. The resulting classification algorithm is characterized by highperformance, similar to the ones produced by alternative methods in the literature, and parsimonious solutions, with a much smaller set of prototypes per class when compared with the other approaches. The number of prototypes is automatically defined by the convergence criterion. The algorithm requires a single user-defined parameter for training, associated with the convergence criterion, and the computational cost is sufficiently reduced to support applications involving large data sets.


Title: An Immune and a Gradient-Based Method to Train Multi-Layer Perceptron Neural Networks

Author: Rodrigo Pasti and Leandro de Castro

Abstract: Multi-layer perceptron (MLP) neural network training can be seen as a special case of function approximation, where no explicit model of the data is assumed. In its simplest form, it corresponds to finding an appropriate set of weights that minimize the network training and generalization errors. Various methods can be used to determine these weights, from standard optimization methods (e.g., gradient-based algorithms) to bio-inspired heuristics (e.g., evolutionary algorithms). Focusing on the problem of finding appropriate weight vectors for MLP networks, this paper proposes the use of an immune algorithm and a second-order gradient-based technique to train MLPs. Results are obtained for classification and function approximation tasks and the different approaches are compared in relation to the types of problems they are more suitable for.