Biased Random-key Genetic Algorithms For The Winner Determination Problem In Combinatorial Auctions.

Abstract In this paper, we address the problem of picking a subset of bids in a general combinatorial auction so as to maximize the overall profit using the first-price model. This winner determination problem assumes that a single bidding round is held to determine both the winners and prices to be paid. We introduce six variants of biased random-key genetic algorithms for this problem. Three of them use a novel initialization technique that makes use of solutions of intermediate linear programming relaxations of an exact mixed integer-linear programming model as initial chromosomes of the population. An experimental evaluation compares the effectiveness of the proposed algorithms with the standard mixed linear integer programming formulation, a specialized exact algorithm, and the best-performing heuristics proposed for this problem. The proposed algorithms are competitive and offer strong results, mainly for large-scale auctions.

Genetic Background of Patients from a University Medical Center in Manhattan: Implications for Personalized Medicine

Background: The rapid progress currently being made in genomic science has created interest in potential clinical applications; however, formal translational research has been limited thus far. Studies of population genetics have demonstrated substantial variation in allele frequencies and haplotype structure at loci of medical relevance and the genetic background of patient cohorts may often be complex. Methods and Findings: To describe the heterogeneity in an unselected clinical sample we used the Affymetrix 6.0 gene array chip to genotype self-identified European Americans (N = 326), African Americans (N = 324) and Hispanics (N = 327) from the medical practice of Mount Sinai Medical Center in Manhattan, NY. Additional data from US minority groups and Brazil were used for external comparison. Substantial variation in ancestral origin was observed for both African Americans and Hispanics; data from the latter group overlapped with both Mexican Americans and Brazilians in the external data sets. A pooled analysis of the African Americans and Hispanics from NY demonstrated a broad continuum of ancestral origin making classification by race/ethnicity uninformative. Selected loci harboring variants associated with medical traits and drug response confirmed substantial within-and between-group heterogeneity. Conclusion: As a consequence of these complementary levels of heterogeneity group labels offered no guidance at the individual level. These findings demonstrate the complexity involved in clinical translation of the results from genome-wide association studies and suggest that in the genomic era conventional racial/ethnic labels are of little value.

GOOAL: A routine based on Genetic Algorithms to track harmonics in electric power systems

Voltage and current waveforms of a distribution or transmission power system are not pure sinusoids. There are distortions in these waveforms that can be represented as a combination of the fundamental frequency, harmonics and high frequency transients. This paper presents a novel approach to identifying harmonics in power system distorted waveforms. The proposed method is based on Genetic Algorithms, which is an optimization technique inspired by genetics and natural evolution. GOOAL, a specially designed intelligent algorithm for optimization problems, was successfully implemented and tested. Two kinds of representations concerning chromosomes are utilized: binary and real. The results show that the proposed method is more precise than the traditional Fourier Transform, especially considering the real representation of the chromosomes.

Reconfiguration of distribution networks to minimize loss and disruption costs using genetic algorithms

In this paper a computational implementation of an evolutionary algorithm (EA) is shown in order to tackle the problem of reconfiguring radial distribution systems. The developed module considers power quality indices such as long duration interruptions and customer process disruptions due to voltage sags, by using the Monte Carlo simulation method. Power quality costs are modeled into the mathematical problem formulation, which are added to the cost of network losses. As for the EA codification proposed, a decimal representation is used. The EA operators, namely selection, recombination and mutation, which are considered for the reconfiguration algorithm, are herein analyzed. A number of selection procedures are analyzed, namely tournament, elitism and a mixed technique using both elitism and tournament. The recombination operator was developed by considering a chromosome structure representation that maps the network branches and system radiality, and another structure that takes into account the network topology and feasibility of network operation to exchange genetic material. The topologies regarding the initial population are randomly produced so as radial configurations are produced through the Prim and Kruskal algorithms that rapidly build minimum spanning trees. (C) 2009 Elsevier B.V. All rights reserved.

Hormonal modulation of photomorphogenesis-controlled anthocyanin accumulation in tomato (Solanum lycopersicum L. cv Micro-Tom) hypocotyls: Physiological and genetic studies

Hormones are likely to be important factors modulating the light-dependent anthocyanin accumulation. Here we analyzed anthocyanin contents in hypocotyls of near isogenic Micro-Tom (MT) tomato lines carrying hormone and phytochrome mutations, as single and double-mutant combinations. In order to recapitulate mutant phenotype, exogenous hormone applications were also performed Anthocyanin accumulation was promoted by exogenous abscisic acid (ABA) and inhibited by gibberellin (GA), in accordance to the reduced anthocyanin contents measured in ABA-deficient (notabills) and GA-constitutive response (procera) mutants. Exogenous cytokinin also enhanced anthocyanin levels in MT hypocotyls. Although auxin-insensitive chageotropica mutant exhibited higher anthocyanin contents, pharmacological approaches employing exogenous auxin and a transport inhibitor did not support a direct role of the hormone in anthocyanin accumulation Analysis of mutants exhibiting increased ethylene production (epwastic) or reduced sensitivity (Never ripe), together with pharmacological data obtained from plants treated with the hormone, indicated a limited role for ethylene in anthocyanin contents. Phytochrome-deficiency (aurea) and hormone double-mutant combinations exhibited phenotypes suggesting additive or synergistic interactions, but not fully espistatic ones, in the control of anthocyanin levels in tomato hypocotyls. Our results indicate that phytochrome-mediated anthocyanin accumulation in tomato hypocotyls is modulated by distinct hormone classes via both shared and independent pathways. (C) 2010 Elsevier Ireland Ltd. All rights reserved

Evaluation of algorithms used to order markers on genetic maps

When building genetic maps, it is necessary to choose from several marker ordering algorithms and criteria, and the choice is not always simple. In this study, we evaluate the efficiency of algorithms try (TRY), seriation (SER), rapid chain delineation (RCD), recombination counting and ordering (RECORD) and unidirectional growth (UG), as well as the criteria PARF (product of adjacent recombination fractions), SARF (sum of adjacent recombination fractions), SALOD (sum of adjacent LOD scores) and LHMC (likelihood through hidden Markov chains), used with the RIPPLE algorithm for error verification, in the construction of genetic linkage maps. A linkage map of a hypothetical diploid and monoecious plant species was simulated containing one linkage group and 21 markers with fixed distance of 3 cM between them. In all, 700 F(2) populations were randomly simulated with and 400 individuals with different combinations of dominant and co-dominant markers, as well as 10 and 20% of missing data. The simulations showed that, in the presence of co-dominant markers only, any combination of algorithm and criteria may be used, even for a reduced population size. In the case of a smaller proportion of dominant markers, any of the algorithms and criteria (except SALOD) investigated may be used. In the presence of high proportions of dominant markers and smaller samples (around 100), the probability of repulsion linkage increases between them and, in this case, use of the algorithms TRY and SER associated to RIPPLE with criterion LHMC would provide better results. Heredity (2009) 103, 494-502; doi:10.1038/hdy.2009.96; published online 29 July 2009

Quantitative Trait Loci Mapping and The Genetic Basis of Heterosis in Maize and Rice

Despite its importance to agriculture, the genetic basis of heterosis is still not well understood. The main competing hypotheses include dominance, overdominance, and epistasis. NC design III is an experimental design that. has been used for estimating the average degree of dominance of quantitative trait 106 (QTL) and also for studying heterosis. In this study, we first develop a multiple-interval mapping (MIM) model for design III that provides a platform to estimate the number, genomic positions, augmented additive and dominance effects, and epistatic interactions of QTL. The model can be used for parents with any generation of selling. We apply the method to two data sets, one for maize and one for rice. Our results show that heterosis in maize is mainly due to dominant gene action, although overdominance of individual QTL could not completely be ruled out due to the mapping resolution and limitations of NC design III. For rice, the estimated QTL dominant effects could not explain the observed heterosis. There is evidence that additive X additive epistatic effects of QTL could be the main cause for the heterosis in rice. The difference in the genetic basis of heterosis seems to be related to open or self pollination of the two species. The MIM model for NC design III is implemented in Windows QTL Cartographer, a freely distributed software.

Review of genetic and epigenetic alterations in hepatocarcinogenesis

Hepatocellular carcinoma (HCC) is associated with multiple risk factors and is believed to arise from pre-neoplastic lesions, usually in the background of cirrhosis. However, the genetic and epigenetic events of hepatocarcinogenesis are relatively poorly understood. HCC display gross genomic alterations, including chromosomal instability (CIN), CpG island methylation, DNA rearrangements associated with hepatitis B virus (HBV) DNA integration, DNA hypomethylation and, to a lesser degree, microsatellite instability. Various studies have reported CIN at chromosomal regions, 1p, 4q, 5q, 6q, 8p, 10q, 11p, 16p, 16q, 17p and 22q. Frequent promoter hypermethylation and subsequent loss of protein expression has also been demonstrated in HCC at tumor suppressor gene (TSG), p16, p14, p15, SOCS1, RIZ1, E-cadherin and 14-3-3 sigma. An interesting observation emerging from these studies is the presence of a methylator phenotype in hepatocarcinogenesis, although it does not seem advantageous to have high levels of microsatellite instability. Methylation also appears to be an early event, suggesting that this may precede cirrhosis. However, these genes have been studied in isolation and global studies of methylator phenotype are required to assess the significance of epigenetic silencing in hepatocarcinogenesis. Based on previous data there are obvious fundamental differences in the mechanisms of hepatic carcinogenesis, with at least two distinct mechanisms of malignant transformation in the liver, related to CIN and CpG island methylation. The reason for these differences and the relative importance of these mechanisms are not clear but likely relate to the etiopathogenesis of HCC. Defining these broad mechanisms is a necessary prelude to determine the timing of events in malignant transformation of the liver and to investigate the role of known risk factors for HCC.

Genetic algorithms for continuous optimization problems - a concept of parameter-space size adjustment

The concept of parameter-space size adjustment is pn,posed in order to enable successful application of genetic algorithms to continuous optimization problems. Performance of genetic algorithms with six different combinations of selection and reproduction mechanisms, with and without parameter-space size adjustment, were severely tested on eleven multiminima test functions. An algorithm with the best performance was employed for the determination of the model parameters of the optical constants of Pt, Ni and Cr.

Genetic analysis of forest species Eugenia uniflora L. through of newly developed SSR markers

Nine microsatellite loci for genetic analysis of three populations of the tropical tree Eugenia uniflora L. (pitanga or Brazilian cherry) from fragments of semideciduous forest were developed. We used the technique of building a (GA)(n) and (CA)(n) microsatellite-enriched library by capture with streptavidin-coated magnetic beads. We assessed the polymorphism of seven microsatellites in 84 mature trees found in three areas (Ribeir (a) over tildeo Preto, Tambau and S (a) over tildeo Jose do Rio Pardo), highly impacted by the agricultural practices, in a large region among Pardo river and Mogi-Guacu river basins, in state of S (a) over tildeo Paulo, Brazil. All loci were polymorphic, and the number of alleles was high, ranging from 6 to 24, with a mean of 14.4. All stands showed the same high level of genetic diversity (mean H(E) = 0.83) and a low genetic differentiation (mean F(ST) = 0.031), indicating that genetic diversity was higher within rather than among populations. Seven of the nine loci were highly variable, and sufficiently informative for E. uniflora. It was concluded that these new SSR markers can be efficiently used for gene flow studies.

Optimal fuzzy PID control tuned with genetic algorithms

Fuzzy logic controllers (FLC) are intelligent systems, based on heuristic knowledge, that have been largely applied in numerous areas of everyday life. They can be used to describe a linear or nonlinear system and are suitable when a real system is not known or too difficult to find their model. FLC provide a formal methodology for representing, manipulating and implementing a human heuristic knowledge on how to control a system. These controllers can be seen as artificial decision makers that operate in a closed-loop system, in real time. The main aim of this work was to develop a single optimal fuzzy controller, easily adaptable to a wide range of systems – simple to complex, linear to nonlinear – and able to control all these systems. Due to their efficiency in searching and finding optimal solution for high complexity problems, GAs were used to perform the FLC tuning by finding the best parameters to obtain the best responses. The work was performed using the MATLAB/SIMULINK software. This is a very useful tool that provides an easy way to test and analyse the FLC, the PID and the GAs in the same environment. Therefore, it was proposed a Fuzzy PID controller (FL-PID) type namely, the Fuzzy PD+I. For that, the controller was compared with the classical PID controller tuned with, the heuristic Ziegler-Nichols tuning method, the optimal Zhuang-Atherton tuning method and the GA method itself. The IAE, ISE, ITAE and ITSE criteria, used as the GA fitness functions, were applied to compare the controllers performance used in this work. Overall, and for most systems, the FL-PID results tuned with GAs were very satisfactory. Moreover, in some cases the results were substantially better than for the other PID controllers. The best system responses were obtained with the IAE and ITAE criteria used to tune the FL-PID and PID controllers.

Genetic diversity of arginine catabolic mobile element in Staphylococcus epidermidis

PLos One, 4(11): ARTe7722