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.

Soft-computing techniques applied to artificial tissue temperature estimation

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

Genetic diversity assessment of the almond (Prunus dulcis (Mill.) DA Webb) traditional germplasm of Algarve, Portugal, using molecular markers

In this study, 123 almond (Prunus dulcis (Mill.) D. A. Webb) trees identified among traditional orchards in the Algarve region and 53 trees of the local field collection managed by the regional office of the Portuguese Ministry of Agriculture (DRAALG) were assessed using isozyme, inter- single sequence repeat and simple sequence repeat or microsatellite techniques for the evaluation of genetic diversity and genetic relatedness and identification of new accessions for the field collection. The isozyme analysis allowed the distribution of the 176 plants into 13 different classes of enzyme similarity, while the use of DNA markers increased the distribution of the analysed trees among 140 discriminating DNA patterns. Multiple cases of homonymy and synonymy were identified in the local germplasm. Some traditional varieties, such as Lourencinha, appeared to be relatively homogeneous, while other local denominations, e.g. Galamba, included diverse genotypes. Of the 13 commercial varieties analysed in this study, 11 assembled in one major cluster clearly differentiated from the majority of the local genotypes. These results reinforced the perception that the Algarve traditional germplasm constitutes an important repository of genetic diversity, eventually carrying alleles of high agricultural interest such as the recently identified Phomopsis resistance in the traditional variety Barrinho Grado.