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The optimization process implements a genetic algorithm to rapidly converge on the channel geometry that provides the highest net power output from the cell.
The complexity of this search was made possible by using sophisticated statistical software, namely the R package glmulti (R Core Team 2016) which implements a genetic algorithm search over large model spaces.
This article introduces the "CAM-Brain Machine" (CBM), an FPGA-based piece of hardware that implements a genetic algorithm (GA) to evolve a cellular automata (CA -based neural network CA -basedodule (of approximately 1000 neurals) inetworkds (i.e., a circuite run of a GA, with 10,000s of circuit growths and performoduleevaluatiofs).
ML searches were conducted with GARLI (version 0.96b8, www.bio.utexas.edu/faculty/antisense/garli/Garli.html), which implements a genetic algorithm to perform rapid heuristic ML searches.
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We design and implement a genetic algorithm (GA) based heuristic.
The minimization over the class of designs is carried out by implementing a genetic algorithm.
Implementing a Genetic Algorithm search strategy we show, using simulated fires, that we recover the five parameter values that characterize fire propagation.
The optimization procedure is developed by means of the coupling between MatLab and EnergyPlus, by implementing a genetic algorithm, and it allows the evaluation of profitable and feasible packages of energy efficiency measures applied to buildings.
In fact, the main purpose of implementing a genetic algorithm is to determine the rules of designing fuzzy-based control approach in such a way that the control does not need information of the expert and be able to work, efficiently.
In this study, we implemented a genetic algorithm (GA) technique to select optimal variables from questionnaire data for predicting a five-year mortality.
To this end, a particular curve model was developed and implemented within a genetic algorithm.
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