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.

Stochastic ground motion simulation with geological site effects in damage asessment

Damage assessment of structures with a mechanical non linear model demands the representation of seismic action in terms of an accelerogram (dynamic analysis) or a response spectrum (pushover analysis). Stochastic ground motion simulation is largely used in regions where seismic strong-motion records are available in insufficient number. In this work we present a variation of the stochastic finite-fault method with dynamic corner frequency that includes the geological site effects. The method was implemented in a computer program named SIMULSIS that generate time series (accelerograms) and response spectra. The program was tested with the MW= 7.3 Landers earthquake (June 28, 1992) and managed to reproduce its effects. In the present work we used it to reproduce the effects of the 1980’s Azores earthquake (January 1, 1980) in several islands, with different possible local site conditions. In those places, the response spectra are presented and compared with the buildings damage observed.

Design of B-spline neural networks using a bacterial programming approach

The design phase of B-spline neural networks represents a very high computational task. For this purpose, heuristics have been developed, but have been shown to be dependent on the initial conditions employed. In this paper a new technique, Bacterial Programming, is proposed, whose principles are based on the replication of the microbial evolution phenomenon. The performance of this approach is illustrated and compared with existing alternatives.

A computational study of a parallel Branch and Bound algorithm for the quadratic 0-1 programming problem on transputers

Discrete optimization problems are very difficult to solve, even if the dimantion is small. For most of them the problem of finding an ε-approximate solution is already NP-hard.

Methodological aspects of a mathematical programming model to evaluate soil tillage technologies in a risky environment

In this work we develop a methodology for the economic evaluation of soil tillage technologies, in a risky environment, and to capture the influence of farmer behaviour on his technology choice. The model has short-term activities, that change with the type of year, and long-term activities, in which sets of traction investment activities are included. Although these activities do not change with the type of year, they lead to different availability of resources for each type of year, since the same tractor has different available fieldwork days under different weather conditions. We prove that the model is sensitive to the greater income variability resulting from the use of alternative technologies and to the balance between income and risk, accounting for the probability of occurrence of each state of nature and giving an investment solution that considers the best production plan for each type of year. (c) 2005 Elsevier B.V. All rights reserved.

Early nutritional programming in fish: tailoring the metabolic use of dietary carbohydrates

Tese de doutoramento, Ciências da Vida, do Mar, da Terra e do Ambiente (Nutrição), Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015