The Experts below are selected from a list of 249 Experts worldwide ranked by ideXlab platform
Olivier Ladislas De Weck - One of the best experts on this subject based on the ideXlab platform.
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Variable Chromosome Length genetic algorithm for progressive refinement in topology optimization
Structural and Multidisciplinary Optimization, 2005Co-Authors: I.y. Kim, Olivier Ladislas De WeckAbstract:This article introduces variable Chromosome Lengths (VCL) in the \ncontext of a genetic algorithm (GA). This concept is applied to\nstructural topology optimization but is also suitable to a broader\nclass of design problems. In traditional genetic algorithms, the\nChromosome Length is determined a priori when the phenotype is\nencoded into the corresponding genotype. Subsequently, the Chromosome\nLength does not change. This approach does not effectively solve\nproblems with large numbers of design variables in complex design\nspaces such as those encountered in structural topology optimization.\nWe propose an alternative approach based on a progressive refinement\nstrategy, where a GA starts with a short Chromosome and first finds\nan "optimum" solution in the simple design space. The "optimum"\nsolutions are then transferred to the following stages with longer\nChromosomes, while maintaining diversity in the population. Progressively\nrefined solutions are obtained in subsequent stages. A strain energy\nfilter is used in order to filter out inefficiently used design\ncells such as protrusions or isolated islands. The variable Chromosome\nLength genetic algorithm (VCL-GA) is applied to two structural\ntopology optimization problems: a short cantilever and a bridge\nproblem. The performance of the method is compared to a brute-force\napproach GA, which operates ab initio at the highest level of resolution.
Muhammad Marwan Muhammad Fuad - One of the best experts on this subject based on the ideXlab platform.
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DEXA (2) - Variable-Chromosome-Length Genetic Algorithm for Time Series Discretization
Lecture Notes in Computer Science, 2016Co-Authors: Muhammad Marwan Muhammad FuadAbstract:The symbolic aggregate approximation method (SAX) of time series is a widely-known dimensionality reduction technique of time series data. SAX assumes that normalized time series have a high-Gaussian distribution. Based on this assumption SAX uses statistical lookup tables to determine the locations of the breakpoints on which SAX is based. In a previous work, we showed how this assumption oversimplifies the problem, which may result in high classification errors. We proposed an alternative approach, based on the genetic algorithms, to determine the locations of the breakpoints. We also showed how this alternative approach boosts the performance of the original SAX. However, the method we presented has the same drawback that existed in the original SAX; it was only able to determine the locations of the breakpoints but not the corresponding alphabet size, which had to be input by the user in the original SAX. In the method we previously presented we had to run the optimization process as many times as the range of the alphabet size. Besides, performing the optimization process in two steps can cause overfitting. The novelty of the present work is twofold; first, we extend a version of the genetic algorithms that uses Chromosomes of different Lengths. Second, we apply this new version of variable-Chromosome-Length genetic algorithm to the problem at hand to simultaneously determine the number of the breakpoints, together with their locations, so that the optimization process is run only once. This speeds up the training stage and also avoids overfitting. The experiments we conducted on a variety of datasets give promising results.
I.y. Kim - One of the best experts on this subject based on the ideXlab platform.
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Variable Chromosome Length genetic algorithm for progressive refinement in topology optimization
Structural and Multidisciplinary Optimization, 2005Co-Authors: I.y. Kim, Olivier Ladislas De WeckAbstract:This article introduces variable Chromosome Lengths (VCL) in the \ncontext of a genetic algorithm (GA). This concept is applied to\nstructural topology optimization but is also suitable to a broader\nclass of design problems. In traditional genetic algorithms, the\nChromosome Length is determined a priori when the phenotype is\nencoded into the corresponding genotype. Subsequently, the Chromosome\nLength does not change. This approach does not effectively solve\nproblems with large numbers of design variables in complex design\nspaces such as those encountered in structural topology optimization.\nWe propose an alternative approach based on a progressive refinement\nstrategy, where a GA starts with a short Chromosome and first finds\nan "optimum" solution in the simple design space. The "optimum"\nsolutions are then transferred to the following stages with longer\nChromosomes, while maintaining diversity in the population. Progressively\nrefined solutions are obtained in subsequent stages. A strain energy\nfilter is used in order to filter out inefficiently used design\ncells such as protrusions or isolated islands. The variable Chromosome\nLength genetic algorithm (VCL-GA) is applied to two structural\ntopology optimization problems: a short cantilever and a bridge\nproblem. The performance of the method is compared to a brute-force\napproach GA, which operates ab initio at the highest level of resolution.
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Variable Chromosome Length Genetic Algorithm for Structural Topology Design Optimization
45th AIAA ASME ASCE AHS ASC Structures Structural Dynamics & Materials Conference, 2004Co-Authors: I.y. Kim, Olivier De WeckAbstract:This article introduces the concept of variable Chromosome Lengths in the context of an adaptive genetic algorithm (GA). This concept is applied to structural topology optimization with large numbers of design variables. In traditional genetic algorithms, the Chromosome Length is determined when the phenotype is encoded into a genotype. Subsequently, the Chromosome Length does not change. This approach does not effectively solve problems with large numbers of design variables and complex design spaces, e.g. structural topology optimization, because the design spaces are extremely large, and it is very difficult to explore the design spaces in their entirety with reasonable population sizes. The proposed GA starts with a short Chromosome and finds an optimum solution in the simple design space. The optimum solution is then transferred to the following stages with a longer Chromosome while maintaining diversity in the population. More refined solutions are obtained in subsequent stages. A strain energy filter is used in order to filter out inefficiently used cells, such as protrusions or isolated islands. The variable Chromosome Length genetic algorithm is applied to structural topology optimization problems of a short cantilever and a bridge problem. The performance of the method is compared with a brute-force approach GA.
Wei-de Zhong - One of the best experts on this subject based on the ideXlab platform.
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Inference of Chromosome-Length Haplotypes Using Genomic Data of Three or a Few More Single Gametes
Molecular biology and evolution, 2020Co-Authors: Jinfeng Chen, Shibo Wang, John M. Chater, Le Zhang, Julong Wei, Yuan-ming Zhang, Wei-de ZhongAbstract:Compared with genomic data of individual markers, haplotype data provide higher resolution for DNA variants, advancing our knowledge in genetics and evolution. Although many computational and experimental phasing methods have been developed for analyzing diploid genomes, it remains challenging to reconstruct Chromosome-scale haplotypes at low cost, which constrains the utility of this valuable genetic resource. Gamete cells, the natural packaging of haploid complements, are ideal materials for phasing entire Chromosomes because the majority of the haplotypic allele combinations has been preserved. Therefore, compared with the current diploid-based phasing methods, using haploid genomic data of single gametes may substantially reduce the complexity in inferring the donor's chromosomal haplotypes. In this study, we developed the first easy-to-use R package, Hapi, for inferring Chromosome-Length haplotypes of individual diploid genomes with only a few gametes. Hapi outperformed other phasing methods when analyzing both simulated and real single gamete cell sequencing data sets. The results also suggested that Chromosome-scale haplotypes may be inferred by using as few as three gametes, which has pushed the boundary to its possible limit. The single gamete cell sequencing technology allied with the cost-effective Hapi method will make large-scale haplotype-based genetic studies feasible and affordable, promoting the use of haplotype data in a wide range of research.
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Inference of Chromosome-Length Haplotypes using Genomic Data of Three to Five Single Gametes
2018Co-Authors: Jen-yung Chen, Shibo Wang, John M. Chater, Le Zhang, Julong Wei, Yong-an Zhang, Wei-de ZhongAbstract:Knowledge of Chromosome-Length haplotypes will not only advance our understanding of the relationship between DNA and phenotypes, but also promote a variety of genetic applications. Here we present Hapi, an innovative method for chromosomal haplotype inference using only 3 to 5 gametes. Hapi outperformed all existing haploid-based phasing methods in terms of accuracy, reliability, and cost efficiency in both simulated and real gamete datasets. This highly cost-effective phasing method will make large-scale haplotype studies feasible to facilitate human disease studies and plant/animal breeding. In addition, Hapi can detect meiotic crossovers in gametes, which has promise in the diagnosis of abnormal recombination activity in human reproductive cells.
Stanisław Cebrat - One of the best experts on this subject based on the ideXlab platform.
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genome analyses and modelling the relationships between coding density recombination rate and Chromosome Length
Journal of Theoretical Biology, 2010Co-Authors: Dorota Mackiewicz, Marta Zawierta, Wojciech Waga, Stanisław CebratAbstract:Abstract In the human genomes, recombination frequency between homologous Chromosomes during meiosis is highly correlated with their physical Length while it differs significantly when their coding density is considered. Furthermore, it has been observed that the recombination events are distributed unevenly along the Chromosomes. We have found that many of such recombination properties can be predicted by computer simulations of population evolution based on the Monte Carlo methods. For example, these simulations have shown that the probability of acceptance of the recombination events by selection is higher at the ends of Chromosomes and lower in their middle parts. The regions of high coding density are more prone to enter the strategy of haplotype complementation and to form clusters of genes, which are “recombination deserts”. The phenomenon of switching in-between the purifying selection and haplotype complementation has a phase transition character, and many relations between the effective population size, coding density, Chromosome size and recombination frequency are those of the power law type.
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Genome analyses and modelling the relationships between coding density, recombination rate and Chromosome Length
Journal of Theoretical Biology, 2010Co-Authors: Dorota Mackiewicz, Marta Zawierta, Wojciech Waga, Stanisław CebratAbstract:In the human genomes, recombination frequency between homologous Chromosomes during meiosis is highly correlated with their physical Length while it differs significantly when their coding density is considered. Furthermore, it has been observed that the recombination events are distributed unevenly along the Chromosomes. We have found that many of such recombination properties can be predicted by computer simulations of population evolution based on the Monte Carlo methods. For example, these simulations have shown that the probability of of the recombination events by selection is higher at the ends of Chromosomes and lower in their middle parts. The regions of high coding density are more prone to enter the strategy of haplotype complementation and to form clusters of genes which are "recombination deserts". The phenomenon of switching in-between the purifying selection and haplotype complementation has a phase transition character, and many relations between the effective population size, coding density, Chromosome size and recombination frequency are those of the power law type.