Results 11 to 20 of about 396,481 (140)
Prototype Selection for Dissimilarity Representation by a Genetic Algorithm [PDF]
2010 20th International Conference on Pattern Recognition, 2010 Dissimilarities can be a powerful way to represent objects like strings, graphs and images for which it is difficult to find good features. The resulting dissimilarity space may be used to train any classifier appropriate for feature spaces. There is, however, a strong need for dimension reduction.
Plasencia-Calana Y. +3 more
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Genetic heterogeneity analysis using genetic algorithm and network science [PDF]
arXiv, 2023 Through genome-wide association studies (GWAS), disease susceptible genetic variables can be identified by comparing the genetic data of individuals with and without a specific disease. However, the discovery of these associations poses a significant challenge due to genetic heterogeneity and feature interactions.
arxiv
Parallelization of Genetic Algorithm with Sexual Selection
IEEJ Transactions on Electronics, Information and Systems, 2003 AbstractWe propose a parallel genetic algorithm with sexual selection. In genetic algorithms with sexual selection with one population, females keep their traits around local optima by using a lower mutation rate than that of the males, while the males change their traits actively.
Satoshi Maekawa +3 more
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Enhanced Direct and Indirect Genetic Algorithm Approaches for a Mall Layout and Tenant Selection Problem [PDF]
Journal of Heuristics, 8(5), pp 503-514, 2002, 2008 During our earlier research, it was recognised that in order to be successful with an indirect genetic algorithm approach using a decoder, the decoder has to strike a balance between being an optimiser in its own right and finding feasible solutions. Previously this balance was achieved manually.
arxiv +1 more source
Genetic Algorithm for variable selection
, 2022 This study aims to explore the coding of GA for variable selection. The selected variables will be later used to build a linear regression model and then report model performance. The study will consider three simple variations of GA based on population size (20, 50, 200) and then plot graphs to show whether any of these variations performed better ...
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Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization [PDF]
arXiv, 2023 Genetic algorithms constitute a family of black-box optimization algorithms, which take inspiration from the principles of biological evolution. While they provide a general-purpose tool for optimization, their particular instantiations can be heuristic and motivated by loose biological intuition.
arxiv
Tag SNP selection via a genetic algorithm
Journal of Biomedical Informatics, 2010 Single Nucleotide Polymorphisms (SNPs) provide valuable information on human evolutionary history and may lead us to identify genetic variants responsible for human complex diseases. Unfortunately, molecular haplotyping methods are costly, laborious, and time consuming; therefore, algorithms for constructing full haplotype patterns from small available
Ghasem Mahdevar +4 more
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Genetic optimization algorithms applied toward mission computability models [PDF]
arXiv, 2020 Genetic algorithms are modeled after the biological evolutionary processes that use natural selection to select the best species to survive. They are heuristics based and low cost to compute. Genetic algorithms use selection, crossover, and mutation to obtain a feasible solution to computational problems.
arxiv
Parameter Selection in Genetic Algorithms
Journal of Systemics, Cybernetics and Informatics, 2004 In this study, we provide a new taxonomy of parameters of genetic algorithms (GA), structural and numerical parameters, and analyze the effect of numerical parameters on the performance of GA based simulation optimization applications with experimental design techniques. Appropriate levels of each parameter are proposed for a particular problem domain.
BOYABATLI, Onur, SABUNCUOGLU, Ihsan
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Selective Mutation For Genetic Algorithms
, 2009 In this paper, we propose a selective mutation method for improving the performances of genetic algorithms. In selective mutation, individuals are first ranked and then additionally mutated one bit in a part of their strings which is selected corresponding to their ranks.
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