Genetic algorithms for modelling and optimisation

McCall, John

doi:10.1016/j.cam.2004.07.034

Cited by 835 publications

(406 citation statements)

References 14 publications

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“…It is difficult to obtain the analytical solution for α 2 , so the optimization algorithm should be employed to compute the optimum solution of α 2 . We use the Standard Genetic Algorithm (SGA) [54] to compute the optimum solution. The objective function of SGA is written as: …”

Section: Continuous Mathematical Model Of the Whitecap Coverage Functmentioning

confidence: 99%

Ocean Wave Rendering with Whitecap in the Visual System of a Maritime Simulator

Chen

Yin

2017

CIT

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The whitecap is an important oceanographic phenomenon. However, existing whitecap rendering methods do not successfully generate realistic whitecaps. To solve this problem, this paper presents a real-time whitecap rendering method applied to the visual system of a maritime simulator. The method takes the vertical acceleration on the wave crest as the criterion of whitecap generation. The Fourier coefficient of the vertical acceleration is provided, and a continuous mathematical model computing the whitecap coverage is built. The vertical acceleration is the variable of the model. The lifetime of the whitecap's existence can be controlled by the parameter of the model, and the parameter is computed with the genetic algorithm. The average of the computed whitecap coverage is equal to the whitecap coverage computed by the stochastic method and is close to the whitecap coverage computed by the empirical formula. The whitecap coverage is used as the blending factor to blend the pixel color of the whitecap texture and that of the sea surface. The presented method has sound theoretical support, with small computational complexity. The rendered whitecap is closer to the description of the Beaufort wind force scale than before.

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Section: Continuous Mathematical Model Of the Whitecap Coverage Functmentioning

confidence: 99%

Ocean Wave Rendering with Whitecap in the Visual System of a Maritime Simulator

Chen

Yin

2017

CIT

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“…John Holland and other people in the United States at the university of Michigan, in order to simulate biological evolution of Darwin, designed a optimization algorithm in 1975 [10] , called Genetic Algorithm (GA), but it was easy to fall into the "premature". This article describes improved Genetic Algorithm, in order to avoid genetic algorithm to fall into "premature", by modifying the cross of Genetic Algorithm.…”

Section: Improved Genetic Optimization Bp-nn Algorithm Designmentioning

confidence: 99%

Fault Diagnosis of Power System Based on Improved Genetic Optimized BP-NN

Mao

Xiang

et al. 2015

MATEC Web of Conferences

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BP neural network (Back-Propagation Neural Network, BP-NN) is one of the most widely neural network models and is applied to fault diagnosis of power system currently. BP neural network has good self-learning and adaptive ability and generalization ability, but the operation process is easy to fall into local minima. Genetic algorithm has global optimization features, and crossover is the most important operation of the Genetic Algorithm. In this paper, we can modify the crossover of traditional Genetic Algorithm, using improved genetic algorithm optimized BP neural network training initial weights and thresholds, to avoid the problem of BP neural network fall into local minima. The results of analysis by an example, the method can efficiently diagnose network fault location, and improve fault-tolerance and grid fault diagnosis effect.

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“…Costa & Oliveira (2001) addressed that evolution strategies such as GA, SAA and ES are emerging as the best algorithm for MNIP problems. Mccall (2005) stated that one of the most attractive features of the GA is its flexibility on handling various objective functions with fewer requirements for fine mathematical properties. Taking into account the above concerns, GA based heuristics is proposed to evolve optimal or near optimal transaction quantity 'x ijopt ' (for ∀i, ∀j) for maximum channel profit to the MV_MBO model.…”

Section: Ga Based Heuristics For Mv_mbomentioning

confidence: 99%