David E. Goldberg. Computer-aided pipeline operation using genetic algorithms and rule learning. PART I: Genetic algorithms in pipeline optimization[J]. Engineering with Computers . 1987 (1)Goldberg, D. E
Gas networks optimization is raised to focus on the network cost regarding to its design parameters after the revolution in personal computers. The aim of the present work is to simulate and optimize gas distribution networks at all pressure ranges, i.e. low, medium and high pressure networks....
MMR gene set was quantified using AUCell, UCell, AddModuleScore, Singsore, and ssGSEA algorithms [20, 21]. The pipeline of the 3 S-MMR ensemble learning model construction In the creation of the 3 S-MMR score, a three-stage stepwise approach was employed. In the Stage 1, we performed...
In their original and most basic form, GAs were used mainly for single objective search and optimization algorithms. Common to most GAs is the use of a chromosome, genetic operators, a selection mechanism and an evaluation mechanism. In our case as Aeroplane wing designers, all we have to do...
KEYWORDSobjectivefunction,geneticalgorithms,optimization,pipesystem I.INTRODUCTION Agreatnumberofpipesaredistributedoutsideoftheaeroenginecase.Theirlocationsanddirections dependonthetechnologicrequirement,thegeometricformofenginemockupan ditssurfacedimens ions . Thesepipesareinterlacedinrestrictedspaceandformanintricatepipen...
Ribeiro, P. et al. (2024). TPOT2: A New Graph-Based Implementation of the Tree-Based Pipeline Optimization Tool for Automated Machine Learning. In: Winkler, S., Trujillo, L., Ofria, C., Hu, T. (eds) Genetic Programming Theory and Practice XX. Genetic and Evolutionary Computation. Spr...
Issues such as representation, efficiency, and convergence became important, because they determine the performance of genetic algorithms in a particular application. By the 1990s, genetic algorithms had become an established optimization method, and have been applied to various engineering problems such ...
2.3. Structural parameters optimization procedure 2.3.1. Genetic algorithm Genetic algorithms (GAs) were inspired by natural selection in evolution (Holland, 1992). The GA used three basic operators to generate high-quality solutions to optimization and search problems: selection, crossover, and mutati...
5). Similar to the GPS map and route planning algorithms in vehicle navigation systems, our constructed QTN map and breeding route optimization paved the way for us to develop a rice genome navigation system: RiceNavi (Fig. 6). Three main modules, including RiceNavi-QTNpick, -Sim and -...
tractography algorithms can vary in their specificity and sensitivity for tract reconstruction75,76. To mitigate these effects, our data processing pipeline has been designed to limit contributions from spurious streamlines72and head motion66. While the accuracy of all tractography methods remains an ope...