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S-systems are mathematical models based on the power-law formalism, which are widely employed for the investigation of Gene Regulatory Networks (GRNs). Because of their complex dynamics-characterized by multi-modality and nonlinearity-The parameterization of S-systems is far from straightforward, demanding global optimization techniques. The problem of parameter estimation of S-systems is further complicated when the desired dynamics is characterized by oscillations. In this work, we describe a novel methodology based on Particle Swarm Optimization for the automatic parameterization of oscillating Ssystems. In this methodology, two swarms perform independent optimizations, and cooperate by periodically exchanging the best particles. The two swarms exploit two different fitness functions: A traditional point-to-point distance, and a spectra-based fitness function. We show that this cooperative approach allows the double swarm to outperform the common methodology, based on a single swarm exploiting a single fitness function. We demonstrate the effectiveness of our method using a GRN of five genes, performing tests of increasing complexity, up to the simultaneous inference of 17 parameters.

Cellular structures with low densities of constituent polymer solids have many physical properties unparalleled in elastomeric materials. Elastomer foams (cellular elastomers) have been widely used in commercial products because of their excellent heat resistance and shock absorbance properties. The finite compressibility of elastomer foams is a key mechanical property that is in contrast with the incompressibility of non-cellular elastomers. The elastomer foams exhibit high reversible deformability like rubber, but unlike rubber, they undergo finite volume change before and after deformation. The values of Poisson's ratio (µ) of low-density elastomer foams are far less than 0.5 for large increase in volume under stretching. A large increase in volume significantly influences multi-axial stress because the volume change has a pronounced effect on dimensional variation under deformation. The mechanical properties of elastomer foams have been mainly investigated in uniaxial compression because of the close relation with their industrial use, but little research has been made to investigate the tensile stress-strain behavior in biaxial deformation. The present work investigate the stress-strain behavior in uniaxial and biaxial extension, and compression of the low-density elastomer foams with open- and closed-cell structures., Cellular structures with low densities of constituent polymer solids have many physical properties unparalleled in elastomeric materials. Elastomer foams (cellular elastomers) have been widely used in commercial products because of their excellent heat resistance and shock absorbance properties. The finite compressibility of elastomer foams is a key mechanical property that is in contrast with the incompressibility of non-cellular elastomers. The elastomer foams exhibit high reversible deformability like rubber, but unlike rubber, they undergo finite volume change before and after deformation. The values of Poisson's ratio (µ) of low-density elastomer foams are far less than 0.5 for large increase in volume under stretching. A large increase in volume significantly influences multi-axial stress because the volume change has a pronounced effect on dimensional variation under deformation. The mechanical properties of elastomer foams have been mainly investigated in uniaxial compression because of the close relation with their industrial use, but little research has been made to investigate the tensile stress-strain behavior in biaxial deformation. The present work investigate the stress-strain behavior in uniaxial and biaxial extension, and compression of the low-density elastomer foams with open- and closed-cell structures.

This chapter introduces a computational strategy to infer a reaction-based models (RBM) that specifically represents a gene regulation model (GRM) characterized by some predefined behavior. It then presents a two-level evolutionary design (ED) methodology, named cuGENED, which integrates two evolutionary computation (EC) algorithms, namely Cartesian genetic programming (CGP) and particle swarm optimization (PSO). The chapter also describes the formalization of GRMs by means of mass-action-based models, and gives a brief introduction of the EC techniques used in the ED of gene regulatory networks (GRNs). Then, it briefly explains the graphics processing units (GPUs) computing framework exploited to speed up the optimization process. The chapter further provides a detailed description of the ED methodology to automatically derive GRMs. Finally, it presents the results of the application of cuGENED for the automatic design of GRNs consisting in two and three genes.

Many feature selection methods are based on the assumption that important features are highly correlated with their corresponding classes, but mainly uncorrelated with each other. Often, this assumption can help eliminate redundancies and produce good predictors using only a small subset of features. However, when the predictability depends on interactions between features, such methods will fail to produce satisfactory results. In this paper a method that can find important features, both independently and dependently discriminative, is introduced. This method works by performing two different types of permutation tests that classify each of the features as either irrelevant, independently predictive or dependently predictive. It was evaluated using a classifier based on an ensemble of genetic programs. The attributes chosen by the permutation tests were shown to yield classifiers at least as good as the ones obtained when all attributes were used during training-and often better. The proposed method also fared well when compared to other attribute selection methods such as RELIEFF and CFS. Furthermore, the ability to determine whether an attribute was independently or dependently predictive was confirmed using artificial datasets with known dependencies., QC 20160519

info:eu-repo/semantics/published

info:eu-repo/semantics/published