Evolving SNP Panels for Genomic Prediction

Whalen, Ian; Banzhaf, Wolfgang; Mamun, Hawlader A. Al; Gondro, Cedric

doi:10.1007/978-3-030-39831-6_31

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…We utilize a version of SA with simultaneous solution comparisons known as Parallel Simulated Annealing (PSA). This is largely due to the inefficient nature of a serial SA within the constraints of this problem, as well as due to the proven ability of similar algorithms such as Differential Evolution to solve larger search spaces, for example by Whalen et al [37]. In order to facilitate this, while still maintaining the core attributes of SA, the algorithm works as can be seen in the Algorithm 1.…”

Section: Parallel Simulated Annealingmentioning

confidence: 99%

A hybrid metaheuristic approach to a real world employee scheduling problem

Reid

Brownlee

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference

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Efficient optimization of resources is paramount to success in many problems faced today. In the field of operational research the efficient scheduling of employees; packing of vans; routing of vehicles; logistics of airlines and transport of materials can be the difference between emission reduction or excess, profits or losses and feasibility or unworkable solutions. The video game Factorio, by Wube Software, has a myriad of problems which are analogous to such real-world problems, and is a useful simulator for developing solutions for these problems. In this paper we define the logistic transport belt problem and define mathematical integer programming model of it. We developed an interface to allow optimizers in any programming language to interact with Factorio, and we provide an initial benchmark of logistic transport belt problems. We present results for Simulated Annealing, quick Genetic Programming and Evolutionary Reinforcement Learning, three different meta-heuristic techniques to optimize this novel problem.

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Section: Parallel Simulated Annealingmentioning

confidence: 99%

A hybrid metaheuristic approach to a real world employee scheduling problem

Reid

Brownlee

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference

View full text Add to dashboard Cite

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Exploring the potential of incremental feature selection to improve genomic prediction accuracy

Heinrich,

Lange,

Kircher

et al. 2023

Genet Sel Evol

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Background The ever-increasing availability of high-density genomic markers in the form of single nucleotide polymorphisms (SNPs) enables genomic prediction, i.e. the inference of phenotypes based solely on genomic data, in the field of animal and plant breeding, where it has become an important tool. However, given the limited number of individuals, the abundance of variables (SNPs) can reduce the accuracy of prediction models due to overfitting or irrelevant SNPs. Feature selection can help to reduce the number of irrelevant SNPs and increase the model performance. In this study, we investigated an incremental feature selection approach based on ranking the SNPs according to the results of a genome-wide association study that we combined with random forest as a prediction model, and we applied it on several animal and plant datasets. Results Applying our approach to different datasets yielded a wide range of outcomes, i.e. from a substantial increase in prediction accuracy in a few cases to minor improvements when only a fraction of the available SNPs were used. Compared with models using all available SNPs, our approach was able to achieve comparable performances with a considerably reduced number of SNPs in several cases. Our approach showcased state-of-the-art efficiency and performance while having a faster computation time. Conclusions The results of our study suggest that our incremental feature selection approach has the potential to improve prediction accuracy substantially. However, this gain seems to depend on the genomic data used. Even for datasets where the number of markers is smaller than the number of individuals, feature selection may still increase the performance of the genomic prediction. Our approach is implemented in R and is available at https://github.com/FelixHeinrich/GP_with_IFS/.

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Evolving SNP Panels for Genomic Prediction

Cited by 2 publications

References 35 publications

A hybrid metaheuristic approach to a real world employee scheduling problem

A hybrid metaheuristic approach to a real world employee scheduling problem

Exploring the potential of incremental feature selection to improve genomic prediction accuracy

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