Fuzzy model identification by evolutionary, gradient based and memtic algorithms

One of the crucial problems of fuzzy rule modeling is how to find an optimal or at least a quasi-optimal rule base fro a certain system. In most applications there is no human expert available, or, the result of a human expert's decision is too much subjective and is not reproducible, thus some automatic method to determine the fuzzy rule base must be deployed.

Design of neuro-fuzzy models by evolutionary and gradient-based algorithms

All systems found in nature exhibit, with different degrees, a nonlinear behavior. To emulate this behavior, classical systems identification techniques use, typically, linear models, for mathematical simplicity. Models inspired by biological principles (artificial neural networks) and linguistically motivated (fuzzy systems), due to their universal approximation property, are becoming alternatives to classical mathematical models. In systems identification, the design of this type of models is an iterative process, requiring, among other steps, the need to identify the model structure, as well as the estimation of the model parameters. This thesis addresses the applicability of gradient-basis algorithms for the parameter estimation phase, and the use of evolutionary algorithms for model structure selection, for the design of neuro-fuzzy systems, i.e., models that offer the transparency property found in fuzzy systems, but use, for their design, algorithms introduced in the context of neural networks. A new methodology, based on the minimization of the integral of the error, and exploiting the parameter separability property typically found in neuro-fuzzy systems, is proposed for parameter estimation. A recent evolutionary technique (bacterial algorithms), based on the natural phenomenon of microbial evolution, is combined with genetic programming, and the resulting algorithm, bacterial programming, advocated for structure determination. Different versions of this evolutionary technique are combined with gradient-based algorithms, solving problems found in fuzzy and neuro-fuzzy design, namely incorporation of a-priori knowledge, gradient algorithms initialization and model complexity reduction.

Genetic diversity and connectivity remain high in Holothuria polii (Delle Chiaje 1823) across a coastal lagoon-open sea environmental gradient

Coastal lagoons represent habitats with widely heterogeneous environmental conditions, particularly as regards salinity and temperature,which fluctuate in both space and time. These characteristics suggest that physical and ecological factors could contribute to the genetic divergence among populations occurring in coastal lagoon and opencoast environments. This study investigates the genetic structure of Holothuria polii at a micro-geographic scale across theMar Menor coastal lagoon and nearbymarine areas, estimating the mitochondrial DNA variation in two gene fragments, cytochrome oxidase I (COI) and 16S rRNA (16S). Dataset of mitochondrial sequences was also used to test the influence of environmental differences between coastal lagoon andmarine waters on population genetic structure. All sampled locations exhibited high levels of haplotype diversity and low values of nucleotide diversity. Both genes showed contrasting signals of genetic differentiation (non-significant differences using COI and slight differences using 16S, which could due to different mutation rates or to differential number of exclusive haplotypes. We detected an excess of recent mutations and exclusive haplotypes, which can be generated as a result of population growth. However, selective processes can be also acting on the gene markers used; highly significant generalized additive models have been obtained considering genetic data from16S gene and independent variables such as temperature and salinity.