Group Leaders Optimization Algorithm

We present a new global optimization algorithm in which the influence of the leaders in social groups is used as an inspiration for the evolutionary technique which is designed into a group architecture. To demonstrate the efficiency of the method, a standard suite of single and multidimensional optimization functions along with the energies and the geometric structures of Lennard-Jones clusters are given as well as the application of the algorithm on quantum circuit design problems. We show that as an improvement over previous methods, the algorithm scales as N^2.5 for the Lennard-Jones clusters of N-particles. In addition, an efficient circuit design is shown for two qubit Grover search algorithm which is a quantum algorithm providing quadratic speed-up over the classical counterpart

Mutagenesis as a Diversity Enhancer and Preserver in Evolution Strategies

Proceedings of: 9th International Symposium on Distributed Computing and Artificial Intelligence (DCAI 2012). Salamanca, March 28-30, 2012Mutagenesis is a process which forces the coverage of certain zones of the search space during the generations of an evolution strategy, by keeping track of the covered ranges for the different variables in the so called gene matrix. Originally introduced as an artifact to control the automated stopping criterion in a memetic algorithm, ESLAT, it also improved the exploration capabilities of the algorithm, even though this was considered a secondary matter and not properly analyzed or tested. This work focuses on this diversity enhancement, redefining mutagenesis to increase this characteristic, measuring this improvement over a set of twenty-seven unconstrained optimization functions to provide statistically significant results.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02.Publicad

VALORACIÓN ECONÓMICA DEL ACEITE DE COCINA DE DESECHO EN EL MUNICIPIO TEXCOCO, MÉXICO

By means of a stratified sampling, the total waste cooking oil (ACD) generated in the central zone of the municipality of Texcoco by the restaurant activity was calculated for the period April 2021 to March 2022, which was 394,564.41 L per year, with a variance of 87.65 units and a standard deviation of 9.36 L and a recovery percentage of 28. 05%, to subsequently apply the environmental valuation method “transfer of benefits” and estimate an economic value of the ACD considering the lowest and maximum market price of biodiesel (transfer good) which was $14.22 MNX and$18.22 MNX giving a value of $5,610,705.91 MNX and$7,188,963.55 MNX annually respectively.Finally, the economic value of the ACD was calculated considering the price that this product presented in the surveys applied in the sampling, which was $8.00 MNX/L, representing a value of$3,156,515.28 MNX annually.Por medio de un muestreo estratificado se calculó el total de aceite de cocina de desecho (ACD) generado en la zona centro del municipio de Texcoco por la actividad restaurantera, en el periodo abril 2021 a marzo 2022, que fue de 394,564.41 L anuales, con una varianza de 87.65 unidades y una desviación estándar de 9.36 L y un porcentaje de recuperación de 28.05%, para posteriormente aplicar el método de valoración ambiental “transferencia de beneficios” y estimar un valor económico del ACD considerando el precio de mercado más bajo y máximo del biodiesel (bien de transferencia) que fue $14.22 MNX y$18.22 MNX dando un valor de $5,610,705.91 MNX y$7,188,963.55 MNX anuales respectivamente.Finalmente se calculó el valor económico del ACD considerando el precio que este producto presentó en las encuestas aplicadas en el muestreo, el cual fue de $8.00 MNX/L, representando un valor de$3,156,515.28 MNX anuales

Distributed evolutionary algorithms and their models: A survey of the state-of-the-art

The increasing complexity of real-world optimization problems raises new challenges to evolutionary computation. Responding to these challenges, distributed evolutionary computation has received considerable attention over the past decade. This article provides a comprehensive survey of the state-of-the-art distributed evolutionary algorithms and models, which have been classified into two groups according to their task division mechanism. Population-distributed models are presented with master-slave, island, cellular, hierarchical, and pool architectures, which parallelize an evolution task at population, individual, or operation levels. Dimension-distributed models include coevolution and multi-agent models, which focus on dimension reduction. Insights into the models, such as synchronization, homogeneity, communication, topology, speedup, advantages and disadvantages are also presented and discussed. The study of these models helps guide future development of different and/or improved algorithms. Also highlighted are recent hotspots in this area, including the cloud and MapReduce-based implementations, GPU and CUDA-based implementations, distributed evolutionary multiobjective optimization, and real-world applications. Further, a number of future research directions have been discussed, with a conclusion that the development of distributed evolutionary computation will continue to flourish

Grammatical evolution decision trees for detecting gene-gene interactions

<p>Abstract</p> <p>Background</p> <p>A fundamental goal of human genetics is the discovery of polymorphisms that predict common, complex diseases. It is hypothesized that complex diseases are due to a myriad of factors including environmental exposures and complex genetic risk models, including gene-gene interactions. Such epistatic models present an important analytical challenge, requiring that methods perform not only statistical modeling, but also variable selection to generate testable genetic model hypotheses. This challenge is amplified by recent advances in genotyping technology, as the number of potential predictor variables is rapidly increasing.</p> <p>Methods</p> <p>Decision trees are a highly successful, easily interpretable data-mining method that are typically optimized with a hierarchical model building approach, which limits their potential to identify interacting effects. To overcome this limitation, we utilize evolutionary computation, specifically grammatical evolution, to build decision trees to detect and model gene-gene interactions. In the current study, we introduce the Grammatical Evolution Decision Trees (GEDT) method and software and evaluate this approach on simulated data representing gene-gene interaction models of a range of effect sizes. We compare the performance of the method to a traditional decision tree algorithm and a random search approach and demonstrate the improved performance of the method to detect purely epistatic interactions.</p> <p>Results</p> <p>The results of our simulations demonstrate that GEDT has high power to detect even very moderate genetic risk models. GEDT has high power to detect interactions with and without main effects.</p> <p>Conclusions</p> <p>GEDT, while still in its initial stages of development, is a promising new approach for identifying gene-gene interactions in genetic association studies.</p

Oblique decision trees for spatial pattern detection: optimal algorithm and application to malaria risk

BACKGROUND: In order to detect potential disease clusters where a putative source cannot be specified, classical procedures scan the geographical area with circular windows through a specified grid imposed to the map. However, the choice of the windows' shapes, sizes and centers is critical and different choices may not provide exactly the same results. The aim of our work was to use an Oblique Decision Tree model (ODT) which provides potential clusters without pre-specifying shapes, sizes or centers. For this purpose, we have developed an ODT-algorithm to find an oblique partition of the space defined by the geographic coordinates. METHODS: ODT is based on the classification and regression tree (CART). As CART finds out rectangular partitions of the covariate space, ODT provides oblique partitions maximizing the interclass variance of the independent variable. Since it is a NP-Hard problem in R(N), classical ODT-algorithms use evolutionary procedures or heuristics. We have developed an optimal ODT-algorithm in R(2), based on the directions defined by each couple of point locations. This partition provided potential clusters which can be tested with Monte-Carlo inference. We applied the ODT-model to a dataset in order to identify potential high risk clusters of malaria in a village in Western Africa during the dry season. The ODT results were compared with those of the Kulldorff' s SaTScan™. RESULTS: The ODT procedure provided four classes of risk of infection. In the first high risk class 60%, 95% confidence interval (CI95%) [52.22–67.55], of the children was infected. Monte-Carlo inference showed that the spatial pattern issued from the ODT-model was significant (p < 0.0001). Satscan results yielded one significant cluster where the risk of disease was high with an infectious rate of 54.21%, CI95% [47.51–60.75]. Obviously, his center was located within the first high risk ODT class. Both procedures provided similar results identifying a high risk cluster in the western part of the village where a mosquito breeding point was located. CONCLUSION: ODT-models improve the classical scanning procedures by detecting potential disease clusters independently of any specification of the shapes, sizes or centers of the clusters

Neural networks for genetic epidemiology: past, present, and future

During the past two decades, the field of human genetics has experienced an information explosion. The completion of the human genome project and the development of high throughput SNP technologies have created a wealth of data; however, the analysis and interpretation of these data have created a research bottleneck. While technology facilitates the measurement of hundreds or thousands of genes, statistical and computational methodologies are lacking for the analysis of these data. New statistical methods and variable selection strategies must be explored for identifying disease susceptibility genes for common, complex diseases. Neural networks (NN) are a class of pattern recognition methods that have been successfully implemented for data mining and prediction in a variety of fields. The application of NN for statistical genetics studies is an active area of research. Neural networks have been applied in both linkage and association analysis for the identification of disease susceptibility genes

A Deeper Look at DES Dwarf Galaxy Candidates: Grus I and Indus II

We present deep g- and r-band Magellan/Megacam photometry of two dwarf galaxy candidates discovered in the Dark Energy Survey (DES), Grus I and Indus II (DES J2038-4609). For the case of Grus I, we resolved the main sequence turn-off (MSTO) and similar to 2 mags below it. The MSTO can be seen at g(0) similar to 24 with a photometric uncertainty of 0.03 mag. We show Grus I to be consistent with an old, metal-poor (similar to 13.3 Gyr, [Fe/H] similar to -1.9) dwarf galaxy. We derive updated distance and structural parameters for Grus I using this deep, uniform, wide-field data set. We find an azimuthally-averaged halflight radius more than two times larger (similar to 151(-31)(+21) pc; similar to 4'. 16(-0.74)(+0.54)) and an absolute V-band magnitude similar to-4.1 that is similar to 1 magnitude brighter than previous studies. We obtain updated distance, ellipticity, and centroid parameters that are in agreement with other studies within uncertainties. Although our photometry of Indus II is similar to 2-3 magnitudes deeper than the DES Y1 public release, we find no coherent stellar population at its reported location. The original detection was located in an incomplete region of sky in the DES Y2Q1 data set and was flagged due to potential blue horizontal branch member stars. The best-fit isochrone parameters are physically inconsistent with both dwarf galaxies and globular clusters. We conclude that Indus II is likely a false positive, flagged due to a chance alignment of stars along the line of sight

Dark Energy Survey Year 3 Results: Deep Field optical + near-infrared images and catalogue

We describe the Dark Energy Survey (DES) Deep Fields, a set of images and associated multiwavelength catalogue (ugrizJHKs) built from Dark Energy Camera (DECam) and Visible and Infrared Survey Telescope for Astronomy (VISTA) data. The DES Deep Fields comprise 11 fields (10 DES supernova fields plus COSMOS), with a total area of ∼30 sq. deg. in ugriz bands and reaching a maximum i-band depth of 26.75 (AB, 10σ, 2 arcsec). We present a catalogue for the DES 3-yr cosmology analysis of those four fields with full 8-band coverage, totalling 5.88 sq. deg. after masking. Numbering 2.8 million objects (1.6 million post-masking), our catalogue is drawn from images coadded to consistent depths of r = 25.7, i = 25, and z = 24.3 mag. We use a new model-fitting code, built upon established methods, to deblend sources and ensure consistent colours across the u-band to Ks-band wavelength range. We further detail the tight control we maintain over the point-spread function modelling required for the model fitting, astrometry and consistency of photometry between the four fields. The catalogue allows us to perform a careful star-galaxy separation and produces excellent photometric redshift performance (NMAD = 0.023 at i < 23). The Deep-Fields catalogue will be made available as part of the cosmology data products release, following the completion of the DES 3-yr weak lensing and galaxy clustering cosmology work