Fernando Nino

Review Article: Recent Advances in Artificial Immune Systems: Models and Applications

The immune system is a remarkable information processing and self learning system that offers inspiration to build artificial immune system (AIS). The field of AIS has obtained a significant degree of success as a branch of Computational Intelligence since it emerged in the 1990s. This paper surveys the major works in the AIS field, in particular, it explores up-to-date advances in applied AIS during the last few years. This survey has revealed that recent research is centered on four major AIS algorithms: (1) negative selection algorithms; (2) artificial immune networks; (3) clonal selection algorithms; (4) Danger Theory and dendritic cell algorithms. However, other aspects of the biological immune system are motivating computer scientists and engineers to develop new models and problem solving methods. Though an extensive amount of AIS applications has been developed, the success of these applications is still limited by the lack of any exemplars that really stand out as killer AIS applications.

A Framework for Evolving Multi-Shaped Detectors in Negative Selection

This paper presents a framework to generate multi-shaped detectors with valued negative selection algorithms (NSA). In particular, detectors can take the form of hyper-rectangles, hyper-spheres and hyper-ellipses in the non-self space. These novel pattern detectors (in the complement space) are evolved using a genetic search (the structured genetic algorithm), which uses hierarchical genomic structures and a gene activation mechanism to encode multiple detector shapes. This genetic search (the structured GA) allows in maintaining diverse shapes while contributing to the proliferation of best suited detector shapes in expressed phenotype. The results showed that a significant coverage of the non-self space could be achieved with fewer detectors compared to other NSA approaches (using only single-shaped detectors). The uniform representation scheme and the evolutionary mechanism used in this work can serve as a baseline for further extension to use several shapes, providing an efficient coverage of non-self space.

A change detection software agent based on immune mixed selection

In this work a software agent based on immune mixed selection is developed. The software agent works in a two dimensional environment represented as a grid. Information about the normal configuration of a path in the environment is considered as the training set of the software agent. The goal of the agent is to learn information about the environment in order to be able to detect any change once it has been trained. A set of detectors, which will characterize the information of the environment, is generated through a learning process based on immunology. Some of the detectors will characterize the positive space (self) while the remaining ones will characterize the negative space (non-self). Some experimental results are presented and compared to other two immune approaches, one based on negative selection and the other on positive selection.

Towards a self-stopping evolutionary algorithm using coupling from the past

In this paper a stopping criterion for a particular class of evolutionary algorithms is devised. First, a model of a generic evolutionary algorithm using iterated random maps is presented. The model allows the exploration of a connection between coupling from the past, and a stopping criterion for evolutionary algorithms. Accordingly, a method to stop a generic evolutionary algorithm is proposed. Some computational experiments are carried out to test the stopping criterion, using a modified version of coupling from the past. Empirical evidence is shown to support the suitability of the criterion.

A Robust Immune Based Approach to the Iterated Prisoner’s Dilemma

In this paper an artificial immune system approach is used to model an agent that plays the Iterated Prisoner’s Dilemma. The learning process during the game is accomplished in two phases: recognition of the opponent’s strategy and selection of the best response. Each phase is carried out using an immune network. Learning abilities of the agent are analyzed, as well as its secondary response and generalization capability. Experimental results show that the immune approach achieved on-line learning; the agent also exhibited robust behavior since it was able to adapt to different environments.

An evolutionary algorithm for fractal coding of binary images

An evolutionary algorithm is used to search for iterated function systems (IFS) that can encode black and white images. As the number of maps of the IFS that encodes an image cannot be known in advance, a variable-length genotype is used to represent candidate solutions, Accordingly, feasibility conditions of the maps are introduced, and special genetic operators that maintain and control their feasibility are defined, In addition, several similarity measures are used to define different fitness functions for experimentation. The performance of the proposed methods is tested on a set of binary images, and experimental results are reported.

A goalkeeper strategy in robot soccer based on Danger Theory

Artificial Immune Systems (AIS) have been successfully modeled and implemented in several engineering applications. In this work, a goalkeeper strategy in robot soccer based on Danger Theory is proposed. Danger Theory is a recent immune theory which has not been widely applied so far. The proposed strategy is implemented and evaluated using middle league SIMUROSOT from FIRA. Experiments carried out yielded promising results.

An Immune-based Multilayered Cognitive Model for Autonomous Navigation

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.

A novel immune anomaly detection technique based on negative selection

In this paper, a novel general anomaly detection technique based on immunology was developed. One main advantage of the AIS is that it starts with a small number of detectors and a new set of antibodies is generated through an iterative process that improves the covering of the self space. The number of detectors generated during the training process was smaller than the size of the input data set because the learning process allows the detectors to have variable radius and it is possible to cover the self space with a small number of detectors. The post-processing of the antibodies improves the performance of the AIS. In future work, the immune technique may be applied to detect other types of intrusions in computer systems, or to solve any other anomaly detection problem.

A multi-objective optimization energy approach to predict the ligand conformation in a docking process

This work proposes a multi-objective algorithmic method for modelling the prediction of the conformation and configuration of ligands in receptor-ligand complexes by considering energy contributions of molecular interactions. The proposed approach is an improvement over others in the field, where the principle insight is that a Pareto front helps to understand the tradeoffs in the actual problem. The method is based on three main features: (i) Representation of molecular data using a trigonometric model; (ii) Modelling of molecular interactions with all-atoms force field energy functions and (iii) Exploration of the conformational space through a multi-objective evolutionary algorithm. The performance of the proposed model was evaluated and validated over a set of well known complexes. The method showed a promising performance when predicting ligands with high number of rotatable bonds.