Optimal design of building structures using genetic algorithms

A general framework for multi-criteria optimal design is presented which is well-suited for automated design of structural systems. A systematic computer-aided optimal design decision process is developed which allows the designer to rapidly evaluate and improve a proposed design by taking into account the major factors of interest related to different aspects such as design, construction, and operation.

The proposed optimal design process requires the selection of the most promising choice of design parameters taken from a large design space, based on an evaluation using specified criteria. The design parameters specify a particular design, and so they relate to member sizes, structural configuration, etc. The evaluation of the design uses performance parameters which may include structural response parameters, risks due to uncertain loads and modeling errors, construction and operating costs, etc. Preference functions are used to implement the design criteria in a "soft" form. These preference functions give a measure of the degree of satisfaction of each design criterion. The overall evaluation measure for a design is built up from the individual measures for each criterion through a preference combination rule. The goal of the optimal design process is to obtain a design that has the highest overall evaluation measure - an optimization problem.

Genetic algorithms are stochastic optimization methods that are based on evolutionary theory. They provide the exploration power necessary to explore high-dimensional search spaces to seek these optimal solutions. Two special genetic algorithms, hGA and vGA, are presented here for continuous and discrete optimization problems, respectively.

The methodology is demonstrated with several examples involving the design of truss and frame systems. These examples are solved by using the proposed hGA and vGA.

Determination of some dominant genetic characteristics and morpho-meristic features for selection of rainbow trout (Oncorhynchus mykiss) broodstocks

In this study, Iranian and French male and female Oncorhynchus mykiss broodstocks were divided into two groups 50 and 24 respectively in Research center of genetic and breeding of coldwater fishes, Yasouj, Iran and the genetic structure of them was investigated using 6 microsatellite markers. Then 19 morphometric and 5 meristic of broodstock were measured and compared in two populations. Along with broodstock maturation, fertilization 1:1(female:male) were randomly assigned and occurred in 25 of 12 Iranian and French treatment respectively. Reproductive parameters were recorded for the whole family. Average number of observed alleles in Iranian and French stocks was 6.68 and 6.83, respectively. Average number of effective alleles in Iranian and French stocks was 3.13 and 3.45 respectively. Fixation index Fst was calculated based on allelic frequency between two stocks was 0.058 with significant difference between 2 stocks. Morphometric analysis showed significant difference between two stocks in 8 characteristics. Meristic characters was without significant difference in broodstock groups. Eyed percentage for french broodstock calculated zero and deleted. Fertilization rate (100-0), the eyed percentage (98- 0), The hatch rate (98-0), the average fecundity 4114.708, the average eggs size 4.88 mm, Survival in the first three months 19-73% calculated for Iranian broodstocks. Considering the quality of eggs and larvae at different stages and selection between the different family and the within family remained 10 treatments and are kept as future broodstocks. The relationship between fecundity - egg size, fecundity - weight , fecundity - length, egg size- weight was performed using regression. The results showed that Fecundity was influenced more by weight and productive length. The research is beginning to ID the broodstock in our country.

Robust estimation of local genetic ancestry in admixed populations using a nonparametric Bayesian approach.

We present a new haplotype-based approach for inferring local genetic ancestry of individuals in an admixed population. Most existing approaches for local ancestry estimation ignore the latent genetic relatedness between ancestral populations and treat them as independent. In this article, we exploit such information by building an inheritance model that describes both the ancestral populations and the admixed population jointly in a unified framework. Based on an assumption that the common hypothetical founder haplotypes give rise to both the ancestral and the admixed population haplotypes, we employ an infinite hidden Markov model to characterize each ancestral population and further extend it to generate the admixed population. Through an effective utilization of the population structural information under a principled nonparametric Bayesian framework, the resulting model is significantly less sensitive to the choice and the amount of training data for ancestral populations than state-of-the-art algorithms. We also improve the robustness under deviation from common modeling assumptions by incorporating population-specific scale parameters that allow variable recombination rates in different populations. Our method is applicable to an admixed population from an arbitrary number of ancestral populations and also performs competitively in terms of spurious ancestry proportions under a general multiway admixture assumption. We validate the proposed method by simulation under various admixing scenarios and present empirical analysis results from a worldwide-distributed dataset from the Human Genome Diversity Project.

Neuro-Fuzzy genetic system for selection of construction project managers

Choosing a project manager for a construction project—particularly, large projects—is a critical project decision. The selection process involves different criteria and should be in accordance with company policies and project specifications. Traditionally, potential candidates are interviewed and the most qualified are selected in compliance with company priorities and project conditions. Precise computing models that could take various candidates’ information into consideration and then pinpoint the most qualified person with a high degree of accuracy would be beneficial. On the basis of the opinions of experienced construction company managers, this paper, through presenting a fuzzy system, identifies the important criteria in selecting a project manager. The proposed fuzzy system is based on IF-THEN rules; a genetic algorithm improves the overall accuracy as well as the functions used by the fuzzy system to make initial estimates of the cluster centers for fuzzy c-means clustering. Moreover, a back-propagation neutral network method was used to train the system. The optimal measures of the inference parameters were identified by calculating the system’s output error and propagating this error within the system. After specifying the system parameters, the membership function parameters—which by means of clustering and projection were approximated—were tuned with the genetic algorithm. Results from this system in selecting project managers show its high capability in making high-quality personnel predictions

A self-adaptive genetic algorithm applied to multi-objective optimization of an airfoil

Genetic algorithms (GAs) have been used to tackle non-linear multi-objective optimization (MOO) problems successfully, but their success is governed by key parameters which have been shown to be sensitive to the nature of the particular problem, incorporating concerns such as the numbers of objectives and variables, and the size and topology of the search space, making it hard to determine the best settings in advance. This work describes a real-encoded multi-objective optimizing GA (MOGA) that uses self-adaptive mutation and crossover, and which is applied to optimization of an airfoil, for minimization of drag and maximization of lift coefficients. The MOGA is integrated with a Free-Form Deformation tool to manage the section geometry, and XFoil which evaluates each airfoil in terms of its aerodynamic efficiency. The performance is compared with those of the heuristic MOO algorithms, the Multi-Objective Tabu Search (MOTS) and NSGA-II, showing that this GA achieves better convergence.

Internal quantum efficiency analysis of solar cell by genetic algorithm

To investigate factors limiting the performance of a GaAs solar cell, genetic algorithm is employed to fit the experimentally measured internal quantum efficiency (IQE) in the full spectra range. The device parameters such as diffusion lengths and surface recombination velocities are extracted. Electron beam induced current (EBIC) is performed in the base region of the cell with obtained diffusion length agreeing with the fit result. The advantage of genetic algorithm is illustrated.

Genetic transformation in Kappaphycus alvarezii using micro-particle bombardment: a potential strategy for germplasm improvement

This study investigated the delivery of a SV40 promoter driving lacZ gene into cells of Kappaphycus alvarezii using particle bombardment. Thallus pieces 0.5-0.8 mm in diameter and 1 cm in length were prepared as gene recipients. Bombardment parameters of 450 psi (rupture pressures) x 6 cm (particle travel distances), 650 psi x 6 cm, 1,100 psi x 6 cm and 1,100 psi x 9 cm were used. A significant increase in transformation efficiency from about 33% under the rupture pressure of 450 psi to 87% at 650 psi was observed in transformed thalli. Most of the positive cells appeared in epidermal cells bombarded at 450 psi, whereas positive signals were seen in both epidermal and medullary cells at 650 psi. No positive transient expression was detected at a bombardment of 1,100 psi, or in negative or blank controls. For the conditions tested, the best parameter was obtained at 650 psi at a distance of 6 cm. Thus, the strategy of taking vegetative thalli as recipients, using particle bombardment, and combining this with micro-propagation, together with developing an in vivo selectable marker, is a viable way to produce stable transformants, to eliminate chimeric expression, and to achieve transgenic breeding in K. alvarezii.

Exponential sensitivity of noise-driven switching in genetic networks

Cells are known to utilize biochemical noise to probabilistically switch between distinct gene expression states. We demonstrate that such noise-driven switching is dominated by tails of probability distributions and is therefore exponentially sensitive to changes in physiological parameters such as transcription and translation rates. However, provided mRNA lifetimes are short, switching can still be accurately simulated using protein-only models of gene expression. Exponential sensitivity limits the robustness of noise-driven switching, suggesting cells may use other mechanisms in order to switch reliably.

Investigation of population genetic structure and mating system in the ant Pheidole pallidula

The origin of eusociality in haplo-diploid organisms such as Hymenoptera has been mostly explained by kin selection. However, several studies have uncovered decreased relatedness values within colonies, resulting primarily from multiple queen matings (polyandry) and/or from the presence of more than one functional queen (polygyny). Here, we report on the use of microsatellite data for the investigation of sociogenetic parameters, such as relatedness, and levels of polygyny and polyandry, in the ant Pheidole pallidula. We demonstrate, through analysis of mother-offspring combinations and the use of direct sperm typing, that each queen is inseminated by a single male. The inbreeding coefficient within colonies and the levels of relatedness between the queens and their mate are not significantly different from zero, indicating that matings occur between unrelated individuals. Analyses of worker genotypes demonstrate that 38% of the colonies are polygynous with 2-4 functional queens, and suggest the existence of reproductive skew, i.e. unequal respective contribution of queens to reproduction. Finally, our analyses indicate that colonies are genetically differentiated and form a population exhibiting significant isolation-by-distance, suggesting that some colonies originate through budding.

Genetic algorithms for local controller network construction

Local Controller Networks (LCNs) provide nonlinear control by interpolating between a set of locally valid, subcontrollers covering the operating range of the plant. Constructing such networks typically requires knowledge of valid local models. This paper describes a new genetic learning approach to the construction of LCNs directly from the dynamic equations of the plant, or from modelling data. The advantage is that a priori knowledge about valid local models is not needed. In addition to allowing simultaneous optimisation of both the controller and validation function parameters, the approach aids transparency by ensuring that each local controller acts independently of the rest at its operating point. It thus is valuable for simultaneous design of the LCNs and identification of the operating regimes of an unknown plant. Application results from a highly nonlinear pH neutralisation process and its associated neural network representation are utilised to illustrate these issues.

Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash

Self-compacting concrete (SCC) flows into place and around obstructions under its own weight to fill the formwork completely and self-compact without any segregation and blocking. Elimination of the need for compaction leads to better quality concrete and substantial improvement of working conditions. This investigation aimed to show possible applicability of genetic programming (GP) to model and formulate the fresh and hardened properties of self-compacting concrete (SCC) containing pulverised fuel ash (PFA) based on experimental data. Twenty-six mixes were made with 0.38 to 0.72 water-to-binder ratio (W/B), 183–317 kg/m³ of cement content, 29–261 kg/m³ of PFA, and 0 to 1% of superplasticizer, by mass of powder. Parameters of SCC mixes modelled by genetic programming were the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength at 7, 28 and 90 days. GP is constructed of training and testing data using the experimental results obtained in this study. The results of genetic programming models are compared with experimental results and are found to be quite accurate. GP has showed a strong potential as a feasible tool for modelling the fresh properties and the compressive strength of SCC containing PFA and produced analytical prediction of these properties as a function as the mix ingredients. Results showed that the GP model thus developed is not only capable of accurately predicting the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength used in the training process, but it can also effectively predict the above properties for new mixes designed within the practical range with the variation of mix ingredients.

Effects of breed and production system on lameness parameters in dairy cattle

The aim of the present study was to assess the effects of Holstein-Friesian (HF) and Norwegian (N) dairy cattle genotypes on lameness parameters in dairy cattle within different production systems over the first 2 lactations. Following calving, HF (n = 39) and N (n = 45) heifers were allocated to 1 of 3 systems of production (high level of concentrate, low level of concentrate, and grass-based). High-and low-concentrate animals were continuously housed indoors on a rotational system so that they spent similar amounts of time on slatted and solid concrete floors. Animals on the grass treatment grazed from spring to autumn in both years of the study, so that most animals on this treatment grazed from around peak to late lactation. Claw health was recorded in both hind claws of each animal at 4 observation periods during each lactation as follows: 1) -8 to 70 d postcalving, 2) 71 to 150 d postcalving, 3) 151 to 225 d postcalving, and 4) 226 to 364 d postcalving. Sole lesions, heel erosion, axial wall deviation, sole length of the right lateral hind claw (claw length), right heel width, and right lateral hind heel height were recorded as well as the presence of digital dermatitis. The N cows had lower (better) white line and total lesion scores than HF cows. Cows on the high-and low-concentrate treatments had better sole and total lesion scores than cows on the grass treatment. The HF cows had better locomotion scores than N cows. Breed and production system differences were observed with respect to claw conformation, including claw length, heel width, and heel height. Digital dermatitis was associated with worse sole lesion scores and interacted with production system to influence white line lesion scores and maximum heel erosion scores. This study shows that genetic, environmental, and infectious factors are associated with hoof pathologies in dairy cows.

Modelling and Control of Shape Memory Alloy Actuators by using Preisach Model, Genetic Algorithm and Fuzzy Logic

Shapememoryalloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications in aeronautics, surgical tools, robotics and so on. Nonlinearity hysteresis effects existing in SMA actuators present a problem in the motion control of these smart actuators. This paper investigates the control problem of SMA actuators in both simulation and experiment. In the simulation, the numerical Preisachmodel with geometrical interpretation is used for hysteresis modeling of SMA actuators. This model is then incorporated in a closed loop PID control strategy. The optimal values of PID parameters are determined by using geneticalgorithm to minimize the mean squared error between desired output displacement and simulated output. However, the control performance is not good compared with the simulation results when these parameters are applied to the real SMA control since the system is disturbed by unknown factors and changes in the surrounding environment of the system. A further automated readjustment of the PID parameters using fuzzylogic is proposed for compensating the limitation. To demonstrate the effectiveness of the proposed controller, real time control experiment results are presented.

Optimisation of Kinetic Parameters for an After-treatment Catalyst

Mathematical modelling has become an essential tool in the design of modern catalytic systems. Emissions legislation is becoming increasingly stringent, and so mathematical models of aftertreatment systems must become more accurate in order to provide confidence that a catalyst will convert pollutants over the required range of conditions. 
Automotive catalytic converter models contain several sub-models that represent processes such as mass and heat transfer, and the rates at which the reactions proceed on the surface of the precious metal. Of these sub-models, the prediction of the surface reaction rates is by far the most challenging due to the complexity of the reaction system and the large number of gas species involved. The reaction rate sub-model uses global reaction kinetics to describe the surface reaction rate of the gas species and is based on the Langmuir Hinshelwood equation further developed by Voltz et al. [1] The reactions can be modelled using the pre-exponential and activation energies of the Arrhenius equations and the inhibition terms. 
The reaction kinetic parameters of aftertreatment models are found from experimental data, where a measured light-off curve is compared against a predicted curve produced by a mathematical model. The kinetic parameters are usually manually tuned to minimize the error between the measured and predicted data. This process is most commonly long, laborious and prone to misinterpretation due to the large number of parameters and the risk of multiple sets of parameters giving acceptable fits. Moreover, the number of coefficients increases greatly with the number of reactions. Therefore, with the growing number of reactions, the task of manually tuning the coefficients is becoming increasingly challenging. 
In the presented work, the authors have developed and implemented a multi-objective genetic algorithm to automatically optimize reaction parameters in AxiSuite®, [2] a commercial aftertreatment model. The genetic algorithm was developed and expanded from the code presented by Michalewicz et al. [3] and was linked to AxiSuite using the Simulink add-on for Matlab. 
The default kinetic values stored within the AxiSuite model were used to generate a series of light-off curves under rich conditions for a number of gas species, including CO, NO, C_{3H8 and C3H6. These light-off curves were used to generate an objective function.} 
This objective function was used to generate a measure of fit for the kinetic parameters. The multi-objective genetic algorithm was subsequently used to search between specified limits to attempt to match the objective function. In total the pre-exponential factors and activation energies of ten reactions were simultaneously optimized. 
The results reported here demonstrate that, given accurate experimental data, the optimization algorithm is successful and robust in defining the correct kinetic parameters of a global kinetic model describing aftertreatment processes.

Codon ambiguities as a mechanism to alter the genetic code in Saccharomyces cerevisiae

Although the genetic code is generally viewed as immutable, alterations to its standard form occur in the three domains of life. A remarkable alteration to the standard genetic code occurs in many fungi of the Saccharomycotina CTG clade where the Leucine CUG codon has been reassigned to Serine by a novel transfer RNA (Ser-tRNACAG). The host laboratory made a major breakthrough by reversing this atypical genetic code alteration in the human pathogen Candida albicans using a combination of tRNA engineering, gene recombination and forced evolution. These results raised the hypothesis that synthetic codon ambiguities combined with experimental evolution may release codons from their frozen state. In this thesis we tested this hypothesis using S. cerevisiae as a model system. We generated ambiguity at specific codons in a two-step approach, involving deletion of tRNA genes followed by expression of non-cognate tRNAs that are able to compensate the deleted tRNA. Driven by the notion that rare codons are more susceptible to reassignment than those that are frequently used, we used two deletion strains where there is no cognate tRNA to decode the rare CUC-Leu codon and AGG-Arg codon. We exploited the vulnerability of the latter by engineering mutant tRNAs that misincorporate Ser at these sites. These recombinant strains were evolved over time using experimental evolution. Although there was a strong negative impact on the growth rate of strains expressing mutant tRNAs at high level, such expression at low level had little effect on cell fitness. We found that not only codon ambiguity, but also destabilization of the endogenous tRNA pool has a strong negative impact in growth rate. After evolution, strains expressing the mutant tRNA at high level recovered significantly in several growth parameters, showing that these strains adapt and exhibit higher tolerance to codon ambiguity. A fluorescent reporter system allowing the monitoring of Ser misincorporation showed that serine was indeed incorporated and possibly codon reassignment was achieved. Beside the overall negative consequences of codon ambiguity, we demonstrated that codons that tolerate the loss of their cognate tRNA can also tolerate high Ser misincorporation. This raises the hypothesis that these codons can be reassigned to standard and eventually to new amino acids for the production of proteins with novel properties, contributing to the field of synthetic biology and biotechnology.