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.

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.

B-spline and neuro-fuzzy models design with function and derivative equalities

The design of neuro-fuzzy models is still a complex problem, as it involves not only the determination of the model parameters, but also its structure. Of special importance is the incorporation of a priori information in the design process. In this paper two known design algorithms for B-spline models will be updated to account for function and derivatives equality restrictions, which are important when the neural model is used for performing single or multi-objective optimization on-line.

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.

Algoritmos construtivos para treino de redes neuronais b-spline

Dissertação de mest. em Engenharia de Sistemas e Computação - Área de Sistemas de Controlo, Faculdade de Ciências e Tecnologia, Univ.do Algarve, 2001

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.

Multilocus genetic analyses provide insight into speciation and hybridization in aquatic grasses, genus Ruppia

Aquatic plants of the genus Ruppia inhabit some of the most threatened habitats in the world, such as coastal lagoons and inland saline to brackish waters where their meadows play several key roles. The evolutionary history of this genus has been affected by the processes of hybridization, polyploidization, and vicariance, which have resulted in uncertainty regarding the number of species. In the present study, we apply microsatellite markers for the identification, genetic characterization, and detection of hybridization events among populations of putative Ruppia species found in the southern Iberian Peninsula, with the exception of a clearly distinct species, the diploid Ruppia maritima. Microsatellite markers group the populations into genetically distinct entities that are not coincident with geographical location and contain unique diagnostic alleles. These results support the interpretation of these entities as distinct species: designated here as (1) Ruppia drepanensis, (2) Ruppia cf. maritima, and (3) Ruppia cirrhosa. A fourth distinct genetic entity was identified as a putative hybrid between R. cf. maritima and R. cirrhosa because it contained a mixture of microsatellite alleles that are otherwise unique to these putative species. Hence, our analyses were able to discriminate among different genetic entities of Ruppia and, by adding multilocus nuclear markers, we confirm hybridization as an important process of speciation within the genus. In addition, careful taxonomic curation of the samples enabled us to determine the genotypic and genetic diversity and differentiation among populations of each putative Ruppia species. This will be important for identifying diversity hotspots and evaluating patterns of population genetic connectivity. © 2015 The Linnean Society of London, Biological Journal of the Linnean Society, 2015, 00, 000–000.