476,845 research outputs found
Genetic algorithm methodology for the estimation of generated power and harmonic content in photovoltaic generation
ProducciĂłn CientĂficaRenewable generation sources like photovoltaic plants are weather dependent and it is hard to predict their behavior. This work proposes a methodology for obtaining a parameterized model that estimates the generated power in a photovoltaic generation system. The proposed methodology uses a genetic algorithm to obtain the mathematical model that best fits the behavior of the generated power through the day. Additionally, using the same methodology, a mathematical model is developed for harmonic distortion estimation that allows one to predict the produced power and its quality. Experimentation is performed using real signals from a photovoltaic system. Eight days from different seasons of the year are selected considering different irradiance conditions to assess the performance of the methodology under different environmental and electrical conditions. The proposed methodology is compared with an artificial neural network, with the results showing an improved performance when using the genetic algorithm methodology.CONACYT (scholarship 415315)FOFI –UAQ 2018 (project FIN201812)PRODEP (project UAQ-PTC-385
Genetic Drift in Genetic Algorithm Selection Schemes
A method for calculating genetic drift in terms of changing population fitness variance is presented. The method allows for an easy comparison of different selection schemes and exact analytical results are derived for traditional generational selection, steady-state selection with varying generation gap, a simple model of Eshelman's CHC algorithm, and evolution strategies. The effects of changing genetic drift on the convergence of a GA are demonstrated empirically
A Novel Genetic Algorithm using Helper Objectives for the 0-1 Knapsack Problem
The 0-1 knapsack problem is a well-known combinatorial optimisation problem.
Approximation algorithms have been designed for solving it and they return
provably good solutions within polynomial time. On the other hand, genetic
algorithms are well suited for solving the knapsack problem and they find
reasonably good solutions quickly. A naturally arising question is whether
genetic algorithms are able to find solutions as good as approximation
algorithms do. This paper presents a novel multi-objective optimisation genetic
algorithm for solving the 0-1 knapsack problem. Experiment results show that
the new algorithm outperforms its rivals, the greedy algorithm, mixed strategy
genetic algorithm, and greedy algorithm + mixed strategy genetic algorithm
Genetic algorithm optimization of entanglement
We present an application of a genetic algorithmic computational method to
the optimization of the concurrence measure of entanglement for the cases of
one dimensional chains, as well as square and triangular lattices in a simple
tight-binding approach in which the hopping of electrons is much stronger than
the phonon dissipationComment: 26 pages with 13 figures, based on Chapter 3 of the Master thesis of
the first author defended at IPICyT, San Luis Potosi, Mx, on 22nd of February
2006, similar to the published version [Fig. 5 left out but contains the
Appendix figure
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