Nonlinear continuous global optimization by modified differential evolution

The task of global optimization is to find a point where the objective function obtains its most extreme value. Differential evolution (DE) is a population-based heuristic approach that creates new candidate solutions by combining several points of the same population. The algorithm has three parameters: amplification factor of the differential variation, crossover control parameter and population size. It is reported that DE is sensitive to the choice of these parameters. To improve the quality of the solution, in this paper, we propose a modified differential evolution introducing self-adaptive parameters, modified mutation and the inversion operator. We test our method with a set of nonlinear continuous optimization problems with simple bounds.Fundação para a Ciência e a Tecnologia (FCT

A simplified binary artificial fish swarm algorithm for 0–1 quadratic knapsack problems

Available online 8 October 2013.This paper proposes a simplified binary version of the artificial fish swarm algorithm (S-bAFSA) for solving 0–1 knapsack problems. This is a combinatorial optimization problem, which arises in many fields of optimization. In S-bAFSA, trial points are created by using crossover and mutation. In order to make the points feasible, a random heuristic drop item procedure is used. The heuristic add item is also implemented to improve the quality of the solutions, and a cyclic reinitialization of the population is carried out to avoid convergence to non-optimal solutions. To enhance the accuracy of the solution, a local search is applied on a predefined number of points. The method is tested on a set of benchmark 0–1 knapsack problems.Fundação para a Ciência e a Tecnologia (FCT

Solving 0–1 quadratic knapsack problems with a population-based artificial fish swarm algorithm

Fundação para a Ciência e a Tecnologia (FCT

A simplified binary artificial fish swarm algorithm for uncapacitated facility location problems

Uncapacitated facility location problem (UFLP) is a combinatorial optimization problem, which has many applications. The artificial fish swarm algorithm has recently emerged in continuous optimization problem. In this paper, we present a simplified binary version of the artificial fish swarm algorithm (S-bAFSA) for solving the UFLP. In S-bAFSA, trial points are created by using crossover and mutation. In order to improve the quality of the solutions, a cyclic reinitialization of the population is carried out. To enhance the accuracy of the solution, a local search is applied on a predefined number of points. The presented algorithm is tested on a set of benchmark uncapacitated facility location problems.Fundação para a Ciência e a Tecnologia (FCT

On Challenging Techniques for Constrained Global Optimization

This chapter aims to address the challenging and demanding issue of solving a continuous nonlinear constrained global optimization problem. We propose four stochastic methods that rely on a population of points to diversify the search for a global solution: genetic algorithm, differential evolution, artificial fish swarm algorithm and electromagnetism-like mechanism. The performance of different variants of these algorithms is analyzed using a benchmark set of problems. Three different strategies to handle the equality and inequality constraints of the problem are addressed. An augmented Lagrangian-based technique, the tournament selection based on feasibility and dominance rules, and a strategy based on ranking objective and constraint violation are presented and tested. Numerical experiments are reported showing the effectiveness of our suggestions. Two well-known engineering design problems are successfully solved by the proposed methods. © Springer-Verlag Berlin Heidelberg 2013.Fundação para a Ciência e a Tecnologia (Foundation for Science and Technology), Portugal for the financial support under fellowship grant: C2007-UMINHO-ALGORITMI-04. The other authors acknowledge FEDER COMPETE, Programa Operacional Fatores de Competitividade (Operational Programme Thematic Factors of Competitiveness) and FCT for the financial support under project grant: FCOMP-01-0124-FEDER-022674info:eu-repo/semantics/publishedVersio

Inferring stabilizing mutations from protein phylogenies : application to influenza hemagglutinin

One selection pressure shaping sequence evolution is the requirement that a protein fold with sufficient stability to perform its biological functions. We present a conceptual framework that explains how this requirement causes the probability that a particular amino acid mutation is fixed during evolution to depend on its effect on protein stability. We mathematically formalize this framework to develop a Bayesian approach for inferring the stability effects of individual mutations from homologous protein sequences of known phylogeny. This approach is able to predict published experimentally measured mutational stability effects (ΔΔG values) with an accuracy that exceeds both a state-of-the-art physicochemical modeling program and the sequence-based consensus approach. As a further test, we use our phylogenetic inference approach to predict stabilizing mutations to influenza hemagglutinin. We introduce these mutations into a temperature-sensitive influenza virus with a defect in its hemagglutinin gene and experimentally demonstrate that some of the mutations allow the virus to grow at higher temperatures. Our work therefore describes a powerful new approach for predicting stabilizing mutations that can be successfully applied even to large, complex proteins such as hemagglutinin. This approach also makes a mathematical link between phylogenetics and experimentally measurable protein properties, potentially paving the way for more accurate analyses of molecular evolution

Fetus-derived DLK1 is required for maternal metabolic adaptations to pregnancy and is associated with fetal growth restriction.

Pregnancy is a state of high metabolic demand. Fasting diverts metabolism to fatty acid oxidation, and the fasted response occurs much more rapidly in pregnant women than in non-pregnant women. The product of the imprinted DLK1 gene (delta-like homolog 1) is an endocrine signaling molecule that reaches a high concentration in the maternal circulation during late pregnancy. By using mouse models with deleted Dlk1, we show that the fetus is the source of maternal circulating DLK1. In the absence of fetally derived DLK1, the maternal fasting response is impaired. Furthermore, we found that maternal circulating DLK1 levels predict embryonic mass in mice and can differentiate healthy small-for-gestational-age (SGA) infants from pathologically small infants in a human cohort. Therefore, measurement of DLK1 concentration in maternal blood may be a valuable method for diagnosing human disorders associated with impaired DLK1 expression and to predict poor intrauterine growth and complications of pregnancy.M.A.M.C. was supported by a PhD studentship from the Cambridge Centre for Trophoblast Research. Research was supported by grants from the MRC (MR/J001597/1 and MR/L002345/1), the Medical College of Saint Bartholomew's Hospital Trust, a Wellcome Trust Investigator Award, EpigeneSys (FP7 Health-257082), EpiHealth (FP7 Health-278414), a Herchel Smith Fellowship (N.T.) and NIH grant RO1 DK89989. The contents are the authors' sole responsibility and do not necessarily represent official NIH views. We thank G. Burton for invaluable support, and M. Constância and I. Sandovici (University of Cambridge) for the Meox2-cre mice. We are extremely grateful to all of the participants in the Pregnancy Outcome Prediction study. This work was supported by the NIHR Cambridge Comprehensive Biomedical Research Centre (Women's Health theme) and project grants from the MRC (G1100221) and Sands (Stillbirth and Neonatal Death Charity). The study was also supported by GE Healthcare (donation of two Voluson i ultrasound systems for this study) and by the NIHR Cambridge Clinical Research Facility, where all research visits took place.This is the author accepted manuscript. The final version is available from Nature Publishing Group via https://doi.org/10.1038/ng.369