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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.
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We design and implement a genetic algorithm (GA) based heuristic.
To solve large-scale line planning instances, we also implemented a genetic solution algorithms.
The optimization process implements a genetic algorithm to rapidly converge on the channel geometry that provides the highest net power output from the cell.
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).
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 study successfully implements a genetic framework for cryptic species detection and characterization.
In this study, we implemented a genetic algorithm (GA) technique to select optimal variables from questionnaire data for predicting a five-year mortality.
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