Rule Representation in Distributed Environments with Accepting Networks of Splicing Processors.

This paper presents the model named Accepting Networks of Evolutionary Processors as NP-problem solver inspired in the biological DNA operations. A processor has a rules set, splicing rules in this model,an object multiset and a filters set. Rules can be applied in parallel since there exists a large number of copies of objects in the multiset. Processors can form a graph in order to solve a given problem. This paper shows the network configuration in order to solve the SAT problem using linear resources and time. A rule representation arquitecture in distributed environments can be easily implemented using these networks of processors, such as decision support systems, as shown in the paper

Virtual Membrane Systems

Within the membrane computing research field, there are many papers about software simulations and a few about hardware implementations. In both cases, algorithms for implementing membrane systems in software and hardware that try to take advantages of massive parallelism are implemented. P-systems are parallel and non deterministic systems which simulate membranes behavior when processing information. This paper presents software techniques based on the proper utilization of virtual memory of a computer. There is a study of how much virtual memory is necessary to host a membrane model. This method improves performance in terms of time

String Measure Applied to String Self-Organizing Maps and Networks of Evolutionary Processors

* Supported by projects CCG08-UAM TIC-4425-2009 and TEC2007-68065-C03-02This paper shows some ideas about how to incorporate a string learning stage in self-organizing algorithms. T. Kohonen and P. Somervuo have shown that self-organizing maps (SOM) are not restricted to numerical data. This paper proposes a symbolic measure that is used to implement a string self-organizing map based on SOM algorithm. Such measure between two strings is a new string. Computation over strings is performed using a priority relationship among symbols; in this case, symbolic measure is able to generate new symbols. A complementary operation is defined in order to apply such measure to DNA strands. Finally, an algorithm is proposed in order to be able to implement a string self-organizing map

Polynomial regression using a perceptron with axo-axonic connections

Social behavior is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these network

Differential Evoluiton - Particle Swarm Optimization

This paper shows the Particle Swarm Optimization algorithm with a Differential Evolution. Each candidate solution is sampled uniformly in [!5,5] D, whereDdenotes the search space dimension, and the evolution is performed with a classical PSO algorithm and a classical DE/x/1 algorithm according to a random threshold

Self-Organizing Architectural design based on Morphogenetic Programming.

In this paper, we present our research into self-organizing building algorithms. This idea of self-organization of animal/plants behaviour interests researchers to explore the mechanisms required for this emergent phenomena and try to apply them in other domains. We were able to implement a typical construction algorithm in a 3D simulation environment and reproduce the results of previous research in the area. LSystems, morphogenetic programming and wasp nest building are explained in order to understand self-organizing models. We proposed Grammatical swarm as a good tool to optimize building structures

Self-organizing Routing Algorithm fo Wireless Sensors Networks (WSN) using Ant Colony Optimization (ACO) with Tinyos.

This paper describes the basic tools to work with wireless sensors. TinyOShas a componentbased architecture which enables rapid innovation and implementation while minimizing code size as required by the severe memory constraints inherent in sensor networks. TinyOS's component library includes network protocols, distributed services, sensor drivers, and data acquisition tools ? all of which can be used asia or be further refined for a custom application. TinyOS was originally developed as a research project at the University of California Berkeley, but has since grown to have an international community of developers and users. Some algorithms concerning packet routing are shown. Incar entertainment systems can be based on wireless sensors in order to obtain information from Internet, but routing protocols must be implemented in order to avoid bottleneck problems. Ant Colony algorithms are really useful in such cases, therefore they can be embedded into the sensors to perform such routing task

Polynomial approximation using particle swarm optimization of lineal enhanced neural networks with no hidden layers.

This paper presents some ideas about a new neural network architecture that can be compared to a Taylor analysis when dealing with patterns. Such architecture is based on lineal activation functions with an axo-axonic architecture. A biological axo-axonic connection between two neurons is defined as the weight in a connection in given by the output of another third neuron. This idea can be implemented in the so called Enhanced Neural Networks in which two Multilayer Perceptrons are used; the first one will output the weights that the second MLP uses to computed the desired output. This kind of neural network has universal approximation properties even with lineal activation functions. There exists a clear difference between cooperative and competitive strategies. The former ones are based on the swarm colonies, in which all individuals share its knowledge about the goal in order to pass such information to other individuals to get optimum solution. The latter ones are based on genetic models, that is, individuals can die and new individuals are created combining information of alive one; or are based on molecular/celular behaviour passing information from one structure to another. A swarm-based model is applied to obtain the Neural Network, training the net with a Particle Swarm algorithm

Business Process Optimization in Madrid City Council.

While designing systems and products requires a deep understanding of influences that achieve desirable performance, the need for an efficient and systematic decision-making approach drives the need for optimization strategies. This paper provides the motivation for this topic as well as a description of applications in Computing Center of Madrid city Council. Optimization applications can be found in almost all areas of engineering. Typical problems in process, working with a database, arise in query design, entity model design and concurrent processes. This paper proposes a solution to optimize a night process dealing with millions of records with an overall performance of about eight times in computation time

Oral versus intramuscular administration of vitamin B12 for vitamin B12 deficiency in primary care : a pragmatic, randomised, non-inferiority clinical trial (OB12)

The trial was financed by Ministerio de Sanidad y Consumo Español through their call for independent clinical research, Orden Ministerial SAS/2377, 2010 (EC10-115, EC10-116, EC10-117, EC10-119, EC10-122); CAIBER—Spanish Clinical Research Network, Instituto de Salud Carlos III (ISCIII) (CAI08/010044); and Gerencia Asistencial de Atención Primaria de Madrid. This study is also supported by the Spanish Clinical Research Network (SCReN), funded by ISCIII-Subdirección General de Evaluación y Fomento de la Investigación, project number PT13/0002/0007, within the National Research Program I+D+I 2013-2016 and co-funded with European Union ERDF funds (European Regional Development Fund). This project received a grant for the translation and publication of this article from the Foundation for Biomedical Research and Innovation in Primary Care (FIIBAP) Call 2017 for grants to promote research programs.Objectives To compare the effectiveness of oral versus intramuscular (IM) vitamin B12 (VB12) in patients aged ≥65 years with VB12 deficiency. Design Pragmatic, randomised, non-inferiority, multicentre trial in 22 primary healthcare centres in Madrid (Spain). Participants 283 patients ≥65 years with VB12 deficiency were randomly assigned to oral (n=140) or IM (n=143) treatment arm. Interventions The IM arm received 1 mg VB12 on alternate days in weeks 1–2, 1 mg/week in weeks 3–8 and 1 mg/month in weeks 9–52. The oral arm received 1 mg/day in weeks 1–8 and 1 mg/week in weeks 9–52. Main outcomes Serum VB12 concentration normalisation (≥211 pg/mL) at 8, 26 and 52 weeks. Non-inferiority would be declared if the difference between arms is 10% or less. Secondary outcomes included symptoms, adverse events, adherence to treatment, quality of life, patient preferences and satisfaction. Results The follow-up period (52 weeks) was completed by 229 patients (80.9%). At week 8, the percentage of patients in each arm who achieved normal B12 levels was well above 90%; the differences in this percentage between the oral and IM arm were −0.7% (133 out of 135 vs 129 out of 130; 95% CI: −3.2 to 1.8; p>0.999) by per-protocol (PPT) analysis and 4.8% (133 out of 140 vs 129 out of 143; 95% CI: −1.3 to 10.9; p=0.124) by intention-to-treat (ITT) analysis. At week 52, the percentage of patients who achieved normal B12 levels was 73.6% in the oral arm and 80.4% in the IM arm; these differences were −6.3% (103 out of 112 vs 115 out of 117; 95% CI: −11.9 to −0.1; p=0.025) and −6.8% (103 out of 140 vs 115 out of 143; 95% CI: −16.6 to 2.9; p=0.171), respectively. Factors affecting the success rate at week 52 were age, OR=0.95 (95% CI: 0.91 to 0.99) and having reached VB12 levels ≥281 pg/mL at week 8, OR=8.1 (95% CI: 2.4 to 27.3). Under a Bayesian framework, non-inferiority probabilities (Δ>−10%) at week 52 were 0.036 (PPT) and 0.060 (ITT). Quality of life and adverse effects were comparable across groups. 83.4% of patients preferred the oral route. Conclusions Oral administration was no less effective than IM administration at 8 weeks. Although differences were found between administration routes at week 52, the probability that the differences were below the non-inferiority threshold was very low.Publisher PDFPeer reviewe