Fernando J. Von Zuben

Recent Developments In Biologically Inspired Computing

Recent Developments in Biologically Inspired Computing is necessary reading for undergraduate and graduate students, and researchers interested in knowing the most recent advances in problem-solving techniques inspired by nature. This book covers the most relevant areas in computational intelligence, including evolutionary algorithms, artificial neural networks, artificial immune systems and swarm systems. It also brings together novel and philosophical trends in the exciting fields of artificial life and robotics. This book has the advantage of covering a large number of computational approaches, presenting the state-of-the-art before entering into the details of specific extensions and new developments. Pseudocodes, flow charts and examples of applications are provided of the new approaches presented.

An artificial immune network for multimodal function optimization on dynamic environments

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 the most popular proposals is denoted opt-aiNet (artificial immune network for optimization) and is extended here to deal with time-varying fitness functions. Additional procedures are designed to improve the overall performance and the robustness of the immune-inspired approach, giving rise to a version for dynamic optimization, denoted dopt-aiNet. Firstly, challenging benchmark problems in static multimodal optimization are considered to validate the new proposal. No parameter adjustment is necessary to adapt the algorithm according to the peculiarities of each problem. In the sequence, dynamic environments are considered, and usual evaluation indices are adopted to assess the performance of dopt-aiNet and compare with alternative solution procedures available in the literature.

Immune and Neural Network Models: Theoretical and Empirical Comparisons

This paper brings a detailed mathematical description of an artificial immune network model, named aiNet. The model is implemented in association with graph concepts and hierarchical clustering techniques, and is proposed to perform machine learning, data compression and cluster analysis. Pictorial representations for the aiNet basic units and typical architectures are introduced. The proposed immune network was primarily compared on a theoretical basis with well-known artificial neural networks. Then, the aiNet was applied to a non-linearly separable benchmark and a real-world problem, and the results were compared with that of the self-organizing feature map and with others already presented in the literature.

Applying biclustering to text mining: an immune-inspired approach

With the rapid development of information technology, computers are proving to be a fundamental tool for the organization and classification of electronic texts, given the huge amount of available information. The existent methodologies for text mining apply standard clustering algorithms to group similar texts. However, these algorithms generally take into account only the global similarities between the texts and assign each one to only one cluster, limiting the amount of information that can be extracted from the texts. An alternative proposal capable of solving these drawbacks is the biclustering technique. The biclustering is able to perform clustering of rows and columns simultaneously, allowing a more comprehensive analysis of the texts. The main contribution of this paper is the development of an immune-inspired biclustering algorithm to carry out text mining, denoted BIC-aiNet. BIC-aiNet interprets the biclustering problem as several two-way bipartition problems, instead of considering a single two-way permutation framework. The experimental results indicate that our proposal is able to group similar texts efficiently and extract implicit useful information from groups of texts.

Hierarchical genetic fuzzy systems

Abstract This paper introduces a hierarchical evolutionary approach to optimize the parameters of Takagi–Sugeno (TS) fuzzy systems. The approach includes a least-squares method to determine the parameters of nonlinear consequents. A pruning procedure is developed to avoid redundancy in each rule consequent and to achieve proper representation flexibility. The performance of the hierarchical evolutionary approach is evaluated using function approximation and classification problems. They demonstrate that the evolutionary algorithm, working together with optimization and pruning procedures, provides structurally simple fuzzy systems whose performance seems to be better than the ones produced by alternative approaches.

Coevolutionary genetic fuzzy systems: a hierarchical collaborative approach

Abstract In this paper a coevolutionary genetic approach is devised to support hierarchical, collaborative relations between individuals representing different parameters of Takagi–Sugeno fuzzy models. The coevolutionary approach assumes species to mean partial solutions of fuzzy modeling problems organized into four hierarchical levels. Individuals at each hierarchical level encode membership functions, individual rules, rule-bases and fuzzy systems, respectively. A shared fitness evaluation scheme is used to measure the performance of each individual. Constraints are observed and particular targets are defined throughout the hierarchical levels, with the purpose of promoting the occurrence of valid individuals and inducing rule compactness, rule base consistency, and partition set visibility. The performance of the approach is evaluated via an example of function approximation with noisy data, and a nonlinearly separable classification problem.

Hybridizing mixtures of experts with support vector machines: Investigation into nonlinear dynamic systems identification

Mixture of experts (ME) models comprise a family of modular neural network architectures aiming at distilling complex problems into simple subtasks. This is done by deploying a separate gating module for softly dividing the input space into overlapping regions to be each assigned to one or more expert networks. Conversely, support vector machines (SVMs) refer to kernel-based methods, neural-network-alike models that constitute an approximate implementation of the structural risk minimization principle. Such learning machines follow the simple, but powerful idea of nonlinearly mapping input data into high-dimensional feature spaces wherein a linear decision surface discriminating different regions is properly designed. In this work, we formally characterize and empirically evaluate a novel approach, named as Mixture of Support Vector Machine Experts (MSVME), whose main purpose is to combine the complementary properties of both SVM and ME models. In the formal characterization, an algorithm based on a maximum likelihood criterion is considered for the MSVME training, and we demonstrate that it is possible to train each expert based on an SVM perspective. Regarding the empirical evaluation, simulation results involving nonlinear dynamic system identification problems are reported, contrasting the performance shown by the MSVME approach with that exhibited by conventional SVM and ME models.

Artificial homeostatic system: a novel approach

Many researchers are developing frameworks inspired by natural, especially biological, systems to solve complex real-world problems. This work extends previous work in the field of biologically inspired computing, proposing an artificial endocrine system for autonomous robot navigation. Having intrinsic self-organizing behaviour, the novel artificial endocrine system can be applied to a wide range of problems, particularly those that involve decision making under changing environmental conditions, such as autonomous robot navigation. This work draws on “embodied cognitive science”, including the study of intelligence, adaptivity, homeostasis, and the dynamic aspects of cognition, in order to help lay down fundamental principles and techniques for a novel approach to more biologically plausible artificial homeostatic systems. Results from using the artificial endocrine system to control a simulated robot are presented.

The construction of a Boolean competitive neural network using ideas from immunology

Abstract The immune system is capable of recognizing and responding to microorganisms and molecules that cannot be perceived by our sensory mechanisms, which send stimuli straight into the brain. It performs an accessory role for nervous cognition. This paper main goals are: (1) to show how some immune principles and theories can be used as sources of inspiration to develop novel neural network learning algorithms; (2) to survey the main works from the literature that employ the immune metaphor for the development of neural network architectures; and (3) to illustrate, with a new network model, how this source of inspiration can be actually used to develop a neural network learning algorithm. The novel learning algorithm proposed has the main features of competitive learning, automatic generation of the network structure and binary representation of the connection strengths (weights). The behavior of the algorithm is primarily described using a benchmark task, and some of its potential applications are illustrated using two simple real-world problems and a binary character recognition task. The results show that the network is a promising tool for solving problems that are inherently binary, and also that the immune system provides a new paradigm to search for neural network learning algorithms.

Variation in Mandible Shape in Thrichomys apereoides (Mammalia: Rodentia): Geometric Analysis of a Complex Morphological Structure

The model of development and evolution of complex morphological structures conceived by Atchley and Hall in 1991 (Biol. Rev. 66:101-157), which establishes that changes at the macroscopic, morphogenetic level can be statistically detected as variation in skeletal units at distinct scales, was applied in combination with the formalism of geometric morphometrics to study variation in mandible shape among populations of the rodent species Thrichomys apereoides. The thin-plate spline technique produced geometric descriptors of shape derived from anatomical landmarks in the mandible, which we used with graphical and inferential approaches to partition the contribution of global and localized components to the observed differentiation in mandible shape. A major pattern of morphological differentiation in T. apereoides is attributable to localized components of shape at smaller geometric scales associated with specific morphogenetic units of the mandible. On the other hand, a clinical trend of variation is associated primarily with localized components of shape at larger geometric scales. Morphogenetic mechanisms assumed to be operating to produce the observed differentiation in the specific units of the mandible include mesenchymal condensation differentiation, muscle hypertrophy, and tooth growth. Perspectives for the application of models of morphological evolution and geometric morphometrics to morphologically based systematic biology are considered.