An evolutionary approach to improve connectivity prediction in mobile wireless sensor networks

Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.

Optimizing urban traffic flow using genetic algorithm with petri net analysis as fitness function

This paper describes a new methodology adopted for urban traffic stream optimization. By using Petri net analysis as fitness function of a Genetic Algorithm, an entire urban road network is controlled in real time. With the advent of new technologies that have been published, particularly focusing on communications among vehicles and roads infrastructures, we consider that vehicles can provide their positions and their destinations to a central server so that it is able to calculate the best route for one of them. Our tests concentrate on comparisons between the proposed approach and other algorithms that are currently used for the same purpose, being possible to conclude that our algorithm optimizes traffic in a relevant manner.

Genetic parameter estimates and principal component analysis on performance and carcass traits of a terminal pig sire line

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Multiobjective Genetic Algorithm applied to dengue control

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Genetic identification of interspecific hybrid of Neotropical catfish species (Pseudoplatystoma corruscans vs. Pseudoplatystoma reticulatum) in rivers of Mato Grosso do Sul State, Brazil

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments

Optical flow methods are accurate algorithms for estimating the displacement and velocity fields of objects in a wide variety of applications, being their performance dependent on the configuration of a set of parameters. Since there is a lack of research that aims to automatically tune such parameters, in this work we have proposed an evolutionary-based framework for such task, thus introducing three techniques for such purpose: Particle Swarm Optimization, Harmony Search and Social-Spider Optimization. The proposed framework has been compared against with the well-known Large Displacement Optical Flow approach, obtaining the best results in three out eight image sequences provided by a public dataset. Additionally, the proposed framework can be used with any other optimization technique.

Geoffroy's side-necked turtle [Phrynops geoffroanus (Schweigger, 1812), Testudines: Chelidae] as a model for evolutionary ecotoxicology: relationship between environmental contamination, conditions and genetic variability

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

First genetic identification of Cryptosporidium parvum subtype IIaA14G2R1in beef cattle in Brazil

The presence of Cryptosporidium spp. in a cattle herd registered with an outbreak of diarrhea was investigated and the the molecular subtyping of Cryptosporidium parvum was characterized. Fecal samples from 85 Nellore beef cattle (Bos indicus) were collected and examined with Ziehl-Neelsen modified staining method. Fifty-four cattle (63.52%) had Cryptosporidium spp. oocysts in their feces. Fragments of genes encoding the 18S ribosomal RNA subunit and a 60-kDa glycoprotein (gp60) were amplified by nested PCR accomplished in the 11 most heavily parasitized samples, and the amplicons were sequenced. Eight of the 11 analyzed samples were positive for 18S rRNA sequences and identified monospecific infections with C. parvum. Seven samples were positive for gp60 and identified subtypes IIaA15G2R1 (6/11) and IIaA14G2R1 (1/11). This report is the first for C. parvum subtype IIaA14G2R1 in beef cattle in Brazil.

Evolutionary Diversification of Banded Tube-Dwelling Anemones (Cnidaria; Ceriantharia; Isarachnanthus) in the Atlantic Ocean

The use of molecular data for species delimitation in Anthozoa is still a very delicate issue. This is probably due to the low genetic variation found among the molecular markers (primarily mitochondrial) commonly used for Anthozoa. Ceriantharia is an anthozoan group that has not been tested for genetic divergence at the species level. Recently, all three Atlantic species described for the genus Isarachnanthus of Atlantic Ocean, were deemed synonyms based on morphological simmilarities of only one species: Isarachnanthus maderensis. Here, we aimed to verify whether genetic relationships (using COI, 16S, ITS1 and ITS2 molecular markers) confirmed morphological affinities among members of Isarachnanthus from different regions across the Atlantic Ocean. Results from four DNA markers were completely congruent and revealed that two different species exist in the Atlantic Ocean. The low identification success and substantial overlap between intra and interspecific COI distances render the Anthozoa unsuitable for DNA barcoding, which is not true for Ceriantharia. In addition, genetic divergence within and between Ceriantharia species is more similar to that found in Medusozoa (Hydrozoa and Scyphozoa) than Anthozoa and Porifera that have divergence rates similar to typical metazoans. The two genetic species could also be separated based on micromorphological characteristics of their cnidomes. Using a specimen of Isarachnanthus bandanensis from Pacific Ocean as an outgroup, it was possible to estimate the minimum date of divergence between the clades. The cladogenesis event that formed the species of the Atlantic Ocean is estimated to have occured around 8.5 million years ago (Miocene) and several possible speciation scenarios are discussed.

Efficient Placement of Fault Indicators in an Actual Distribution System Using Evolutionary Computing

This paper proposes an evolutionary computing strategy to solve the problem of fault indicator (FI) placement in primary distribution feeders. More specifically, a genetic algorithm (GA) is employed to search for an efficient configuration of FIs, located at the best positions on the main feeder of a real-life distribution system. Thus, the problem is modeled as one of optimization, aimed at improving the distribution reliability indices, while, at the same time, finding the least expensive solution. Based on actual data, the results confirm the efficiency of the GA approach to the FI placement problem.

Modeling and Identification of an Open-frame Underwater Vehicle: The Yaw Motion Dynamics

A semi-autonomous unmanned underwater vehicle (UUV), named LAURS, is being developed at the Laboratory of Sensors and Actuators at the University of Sao Paulo. The vehicle has been designed to provide inspection and intervention capabilities in specific missions of deep water oil fields. In this work, a method of modeling and identification of yaw motion dynamic system model of an open-frame underwater vehicle is presented. Using an on-board low cost magnetic compass sensor the method is based on the utilization of an uncoupled 1-DOF (degree of freedom) dynamic system equation and the application of the integral method which is the classical least squares algorithm applied to the integral form of the dynamic system equations. Experimental trials with the actual vehicle have been performed in a test tank and diving pool. During these experiments, thrusters responsible for yaw motion are driven by sinusoidal voltage signal profiles. An assessment of the feasibility of the method reveals that estimated dynamic system models are more reliable when considering slow and small sinusoidal voltage signal profiles, i.e. with larger periods and with relatively small amplitude and offset.

Localising and quantifying damage by means of a multi-chromosome genetic algorithm

This paper presents a structural damage detection methodology based on genetic algorithms and dynamic parameters. Three chromosomes are used to codify an individual in the population. The first and second chromosomes locate and quantify damage, respectively. The third permits the self-adaptation of the genetic parameters. The natural frequencies and mode shapes are used to formulate the objective function. A numerical analysis was performed for several truss structures under different damage scenarios. The results have shown that the methodology can reliably identify damage scenarios using noisy measurements and that it results in only a few misidentified elements. (C) 2012 Civil-Comp Ltd and Elsevier Ltd. All rights reserved.

Cash balance management: A comparison between genetic algorithms and particle swarm optimization

This work aimed to apply genetic algorithms (GA) and particle swarm optimization (PSO) in cash balance management using Miller-Orr model, which consists in a stochastic model that does not define a single ideal point for cash balance, but an oscillation range between a lower bound, an ideal balance and an upper bound. Thus, this paper proposes the application of GA and PSO to minimize the Total Cost of cash maintenance, obtaining the parameter of the lower bound of the Miller-Orr model, using for this the assumptions presented in literature. Computational experiments were applied in the development and validation of the models. The results indicated that both the GA and PSO are applicable in determining the cash level from the lower limit, with best results of PSO model, which had not yet been applied in this type of problem.

A multi-objective adaptive immune algorithm for multi-application NoC mapping

Current SoC design trends are characterized by the integration of larger amount of IPs targeting a wide range of application fields. Such multi-application systems are constrained by a set of requirements. In such scenario network-on-chips (NoC) are becoming more important as the on-chip communication structure. Designing an optimal NoC for satisfying the requirements of each individual application requires the specification of a large set of configuration parameters leading to a wide solution space. It has been shown that IP mapping is one of the most critical parameters in NoC design, strongly influencing the SoC performance. IP mapping has been solved for single application systems using single and multi-objective optimization algorithms. In this paper we propose the use of a multi-objective adaptive immune algorithm (M(2)AIA), an evolutionary approach to solve the multi-application NoC mapping problem. Latency and power consumption were adopted as the target multi-objective functions. To compare the efficiency of our approach, our results are compared with those of the genetic and branch and bound multi-objective mapping algorithms. We tested 11 well-known benchmarks, including random and real applications, and combines up to 8 applications at the same SoC. The experimental results showed that the M(2)AIA decreases in average the power consumption and the latency 27.3 and 42.1 % compared to the branch and bound approach and 29.3 and 36.1 % over the genetic approach.

A new evolutionary clustering search for a no-wait flow shop problem with set-up times

This paper addresses the m-machine no-wait flow shop problem where the set-up time of a job is separated from its processing time. The performance measure considered is the total flowtime. A new hybrid metaheuristic Genetic Algorithm-Cluster Search is proposed to solve the scheduling problem. The performance of the proposed method is evaluated and the results are compared with the best method reported in the literature. Experimental tests show superiority of the new method for the test problems set, regarding the solution quality. (c) 2012 Elsevier Ltd. All rights reserved.