F.J. Von Zuben

Learning and optimization using the clonal selection principle

The clonal selection principle is used to explain the basic features of an adaptive immune response to an antigenic stimulus. It establishes the idea that only those cells that recognize the antigens (Ags) are selected to proliferate. The selected cells are subject to an affinity maturation process, which improves their affinity to the selective Ags. This paper proposes a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. The general algorithm, named CLONALG, is derived primarily to perform machine learning and pattern recognition tasks, and then it is adapted to solve optimization problems, emphasizing multimodal and combinatorial optimization. Two versions of the algorithm are derived, their computational cost per iteration is presented, and a sensitivity analysis in relation to the user-defined parameters is given. CLONALG is also contrasted with evolutionary algorithms. Several benchmark problems are considered to evaluate the performance of CLONALG and it is also compared to a niching method for multimodal function optimization.

An evolutionary immune network for data clustering

This paper explores basic aspects of the immune system and proposes a novel immune network model with the main goals of clustering and filtering unlabelled numerical data sets. It is not our concern to reproduce with confidence any immune phenomenon, but to show that immune concepts can be used to develop powerful computational tools for data processing. As important results of our model, the network evolved will be capable of reducing redundancy, describing data structure, including the shape of the clusters. The network will be implemented in association with a statistical inference technique, and its performance will be illustrated using two benchmark problems. The paper is concluded with a trade-off between the proposed network and artificial neural networks used to perform unsupervised learning.This paper explores basic aspects of the immune system and proposes a novel immune network model with the main goals of clustering and filtering unlabelled numerical data sets. It is not our concern to reproduce with confidence any immune phenomenon, but to show that immune concepts can be used to develop powerful computational tools for data processing. As important results of our model, the network evolved will be capable of reducing redundancy, describing data structure, including the shape of the clusters. The network will be implemented in association with a statistical inference technique, and its performance will be illustrated using two benchmark problems. The paper is concluded with a trade-off between the proposed network and artificial neural networks used to perform unsupervised learning.

Decentralized control system for autonomous navigation based on an evolved artificial immune network

This paper investigates an autonomous control system of a mobile robot based on the immune network theory. The immune network navigates the robot to solve a multiobjective task, namely, garbage collection: the robot must find and collect garbage, while it establishes a trajectory without colliding with obstacles, and return to the base before it runs out of energy. Each network node corresponds to a specific antibody and describes a particular control action for the robot. The antigens are the current state of the robot, read from a set of internal and external sensors. The network dynamics corresponds to the variation of antibody concentration levels, which change according to both mutual interaction of antibody nodes and of antibodies and antigens. It is proposed an evolutionary mechanism to determine the network configuration, that is, the parameters that define those interactions. Simulation results suggest that the proposal presented is very promising.

Applying Biclustering to Perform Collaborative Filtering

Collaborative filtering (CF) is a method to perform automated suggestions for a user based on the opinion of other users with similar interest. Most of the CF algorithms do not take into account the existent duality between users and items, considering only the similarities between users or only the similarities between items. In this paper we propose a novel methodology for the CF capable of dealing with this situation. By proposing an immune-inspired bi clustering technique to carry out clustering of rows and columns at the same time, our algorithm is able to group similarities between users and items. In order to evaluate the proposed methodology, we have applied it to Movie Lens dataset which contains users ratings to a large set of movies. The results indicate that our proposal is able to provide useful recommendations for the users, outperforming other methodologies for CF reported in the literature.

Theoretical approaches to forensic entomology: I. Mathematical model of postfeeding larval dispersal

Abstract: An overall theoretical approach to model phenomena of interest for forensic entomology is advanced. Efforts are concentrated in identifying biological attributes at the individual, population and community of the arthropod fauna associated with decomposing human corpses and then incorporating these attributes into mathematical models. In particular in this paper a diffusion model of dispersal of postfeeding larvae is described for blowflies, which are the most common insects associated with corpses.

Towards the evolution of an artificial homeostatic system

This paper presents an artificial homeostatic system (AHS) devoted to the autonomous navigation of mobile robots, with emphasis on neuro-endocrine interactions. The AHS is composed of two modules, each one associated with a particular reactive task and both implemented using an extended version of the GasNet neural model, denoted spatially unconstrained GasNet model or simply non-spatial GasNet (NSGasNet). There is a coordination system, which is responsible for the specific role of each NSGasNet at a given operational condition. The switching among the NSGasNets is implemented as an artificial endocrine system (AES), which is based on a system of coupled nonlinear difference equations. The NSGasNets are synthesized by means of an evolutionary algorithm. The obtained neuro-endocrine controller is adopted in simulated and real benchmark applications, and the additional flexibility provided by the use of NSGasNet, together with the existence of an automatic coordination system, guides to convincing levels of performance.

Makespan minimization on parallel processors: an immune-based approach

This work deals with the problem of scheduling jobs to identical parallel processors with the goal of minimizing the completion time of the last processor to finish its execution (makespan). This problem is known to be NP-Hard. The algorithm proposed here is inspired by the immune systems of vertebrate animals. The advantage of combinatorial optimization algorithms based on artificial immune systems is the inherent ability to preserve a diverse set of near-optimal solutions along the search. The results produced by the method are compared with results of classical heuristics.

Evaluating the Performance of a Biclustering Algorithm Applied to Collaborative Filtering - A Comparative Analysis

Collaborative filtering (CF) is a method to perform automated suggestions for a user based on the opinion of other users with similar interest. Most of the CF algorithms do not take into account the existent duality between users and items, considering only the similarities between users or only the similarities between items. The authors have proposed in a previous work a bio-inspired methodology for CF, namely BIC-aiNet, capable of clustering rows and columns of a data matrix simultaneously. The usefulness and performance of the methodology are reported in the literature. Now, the authors carry out more rigorous comparative experiments with BIC-aiNet and other techniques found in the literature, as well as evaluate the scalability of the algorithm in several datasets of different sizes. The results indicate that our proposal is able to provide useful recommendations for the users, outperforming other methodologies for CF.

Hierarchical fuzzy models within the framework of orthonormal basis functions and their application to bioprocess control

Fuzzy models within the framework of orthonormal basis functions (OBF fuzzy models) have been introduced in previous works and shown to be a very promising approach to the areas of nonlinear system identification and control, since they exhibit several advantages over those dynamic model topologies usually adopted in the literature. As fuzzy models, however, they exhibit the dimensionality problem which is the main drawback to the application of neural networks and fuzzy systems to the modeling and control of large-scale systems. This problem has successfully been dealt with in the literature by means of hierarchical structures composed of submodels connected in cascade. In the present paper a hierarchical fuzzy model within the OBF framework is presented. A data-driven hybrid identification method based on genetic and gradient-based algorithms is described in details. A model-based predictive control scheme is also presented and applied to control of a complex industrial process for ethyl alcohol (ethanol) production.

Dynamics of Experimental Populations of Native and Introduced Blowflies (Diptera: Calliphoridae): Mathematical Modelling and the Transition from Asymptotic Equilibrium to Bounded Oscillations

The equilibrium dynamics of native and introduced blowflies is modelled using a density-dependent model of population growth that takes into account important features of the life-history in these flies. A theoretical analysis indicates that the product of maximum fecundity and survival is the primary determinant of the dynamics. Cochliomyia macellaria, a blowfly native to the Americas and the introduced Chrysomya megacephala and Chrysomya putoria, differ in their dynamics in that the first species shows a damping oscillatory behavior leading to a one-point equilibrium, whereas in the last two species population numbers show a two-point limit cycle. Simulations showed that variation in fecundity has a marked effect on the dynamics and indicates the possibility of transitions from one-point equilibrium to bounded oscillations and aperiodic behavior. Variation in survival has much less influence on the dynamics.