Soft-computing techniques applied to artificial tissue temperature estimation

Tese dout., Engenharia electrónica e computação - Processamento de sinal, Universidade do Algarve, 2008

A study on the deployment of GA in a grid computing framework

Dissertação de Mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015

Sintonia automática de controladores PID: uma abordagem neuro - genética

Dissertação de dout. em Electrónica e Computação, Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2004

Model comparison for temperature estimation inside buildings

This paper presents a comparison between a physical model and an artificial neural network model (NN) for temperature estimation inside a building room. Despite the obvious advantages of the physical model for structure optimisation purposes, this paper will test the performance of neural models for inside temperature estimation. The great advantage of the NN model is a big reduction of human effort time, because it is not needed to develop the structural geometry and structural thermal capacities and to simulate, which consumes a great human effort and great computation time. The NN model deals with this problem as a “black box” problem. We describe the use of the Radial Basis Function (RBF), the training method and a multi-objective genetic algorithm for optimisation/selection of the RBF neural network inputs and number of neurons.

Neuro-genetic PID autotuning: time invariant case

The Proportional, Integral and Derivative (PID) controllers are widely used in induxtrial applications. Their popularity comes from their robust performance and also from their functional simplicity.

Automatic tuning of PID controllers using a neuro-genetic system

Neural networks and genetic algorithms have been in the past successfully applied, separately, to controller turning problems. In this paper we propose to combine its joint use, by exploiting the nonlinear mapping capabilites of neural networks to model objective functions, and to use them to supply their values to a genetic algorithm which performs on-line minimization.

B-splines neural network assisted PID autotuning

This papers describes an extantion of previous works on the subject of neural network proportional, integral and derivative (PID) autotuning. Basically, neural networks are employed to supply the three PID parameters, according to the integral of time multiplied by the absolute error (ITAE) criterion, to a standard PID controller.

New methods for PID autotuning

In this paper a recent approach for PID autotuning, involving neural networks, is ferther developed. To make this approach adaptive, optimal PID values must be known on-line. In this paper neural network models of tuning criteria, together with the use of genetic algorithms, are proposed to solve this problem.

Learning control systems

Novel method of controller (PID) autotuning, involving neural networks and genetic algorithms: to employ neural networks to map the identification measures and controller parameters to objective functions, adapt these models on-line; to employ the genetic algorithm to perform on-line minimization.

Genetic programming and bacterial algorithm for neural networks and fuzzy systems design

In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use.

Genetic and bacterial programming for B-Spline neural networks design

The design phase of B-spline neural networks is a highly computationally complex task. Existent heuristics have been found to be highly dependent on the initial conditions employed. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this paper, the Bacterial Programming approach is presented, which is based on the replication of the microbial evolution phenomenon. This technique produces an efficient topology search, obtaining additionally more consistent solutions.

The effect of redundancy and neutrality in genetic search

An experimental study aimed at assessing the influence of redundancy and neutrality on the performance of an (1+1)-ES evolution strategy modeled using Markov chains and applied to NK fitness landscapes is presented. For the study, two families of redundant binary representations, one non-neutral family which is based on linear transformations and that allows the phenotypic neighborhoods to be designed in a simple and effective way, and the neutral family based on the mathematical formulation of error control codes are used. The results indicate whether redundancy or neutrality affects more strongly the behavior of the algorithm used.

Training neuro-fuzzy models using evolution based algorithms

The normal design process for neural networks or fuzzy systems involve two different phases: the determination of the best topology, which can be seen as a system identification problem, and the determination of its parameters, which can be envisaged as a parameter estimation problem. This latter issue, the determination of the model parameters (linear weights and interior knots) is the simplest task and is usually solved using gradient or hybrid schemes. The former issue, the topology determination, is an extremely complex task, especially if dealing with real-world problems.

Ecological genetics in the North Atlantic: environmental gradients and adaptation at specific loci

The North Atlantic intertidal community provides a rich set of organismal and environmental material for the study of ecological genetics. Clearly defined environmental gradients exist at multiple spatial scales: there are broad latitudinal trends in temperature, meso-scale changes in salinity along estuaries, and smaller scale gradients in desiccation and temperature spanning the intertidal range. The geology and geography of the American and European coasts provide natural replication of these gradients, allowing for population genetic analyses of parallel adaptation to environmental stress and heterogeneity. Statistical methods have been developed that provide genomic neutrality tests of population differentiation and aid in the process of candidate gene identification. In this paper, we review studies of marine organisms that illustrate associations between an environmental gradient and specific genetic markers. Such highly differentiated markers become candidate genes for adaptation to the environmental factors in question, but the functional significance of genetic variants must be comprehensively evaluated. We present a set of predictions about locus-specific selection across latitudinal, estuarine, and intertidal gradients that are likely to exist in the North Atlantic. We further present new data and analyses that support and contradict these simple selection models. Some taxa show pronounced clinal variation at certain loci against a background of mild clinal variation at many loci. These cases illustrate the procedures necessary for distinguishing selection driven by internal genomic vs. external environmental factors. We suggest that the North Atlantic intertidal community provides a model system for identifying genes that matter in ecology due to the clarity of the environmental stresses and an extensive experimental literature on ecological function. While these organisms are typically poor genetic and genomic models, advances in comparative genomics have provided access to molecular tools that can now be applied to taxa with well-defined ecologies. As many of the organisms we discuss have tight physiological limits driven by climatic factors, this synthesis of molecular population genetics with marine ecology could provide a sensitive means of assessing evolutionary responses to climate change.

Genetic structure at range edge: Low diversity and high inbreeding in Southeast Asian mangrove (Avicennia marina) populations

Understanding the genetic composition and mating systems of edge populations provides important insights into the environmental and demographic factors shaping species’ distribution ranges. We analysed samples of the mangrove Avicennia marina from Vietnam, northern Philippines and Australia, with microsatellite markers. We compared genetic diversity and structure in edge (Southeast Asia, and Southern Australia) and core (North and Eastern Australia) populations, and also compared our results with previously published data from core and southern edge populations. Comparisons highlighted significantly reduced gene diversity and higher genetic structure in both margins compared to core populations, which can be attributed to very low effective population size, pollinator scarcity and high environmental pressure at distribution margins. The estimated level of inbreeding was significantly higher in northeastern populations compared to core and southern populations. This suggests that despite the high genetic load usually associated with inbreeding, inbreeding or even selfing may be advantageous in margin habitats due to the possible advantages of reproductive assurance, or local adaptation. The very high level of genetic structure and inbreeding show that populations of A. marina are functioning as independent evolutionary units more than as components of a metapopulation system connected by gene flow. The combinations of those characteristics make these peripheral populations likely to develop local adaptations and therefore to be of particular interest for conservation strategies as well as for adaptation to possible future environmental changes.