Maintaining Healthy Population Diversity Using Adaptive Crossover, Mutation, and Selection

McGinley, Brian; Maher, John; O’Riordan, Colm; Morgan, Frank

doi:10.1109/tevc.2010.2046173

Cited by 116 publications

(80 citation statements)

References 37 publications

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“…The other selection methods include roulette wheel, rank selection and tournament [21]. Roulette wheel is a probability based method, whereas the tournament selection is a winner-takes-all based technique [22].…”

Section: Selectionmentioning

confidence: 99%

Precision Modeling: Application of Metaheuristics on Current–Voltage Curves of Superconducting Films

et al. 2018

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Contemplating the importance of studying current-voltage curves in superconductivity, it has been recently and rightly argued that their approximation, rather than incessant measurements, seems to be a more viable option. This especially becomes bona fide when the latter needs to be recorded for a wide range of critical parameters including temperature and magnetic field, thereby becoming a tedious monotonous procedure. Artificial neural networks have been recently put forth as one methodology for approximating these so-called electrical measurements for various geometries of antidots on a superconducting thin film. In this work, we demonstrate that the prediction accuracy, in terms of mean-squared error, achieved by artificial neural networks is rather constrained, and, due to their immense credence on randomly generated networks' coefficients, they may result in vastly varying prediction accuracies for different geometries, experimental conditions, and their own tunable parameters. This inconsistency in prediction accuracies is resolved by controlling the uncertainty in networks' initialization and coefficients' generation by means of a novel entropy based genetic algorithm. The proposed method helps in achieving a substantial improvement and consistency in the prediction accuracy of current-voltage curves in comparison to existing works, and is amenable to various geometries of antidots, including rectangular, square, honeycomb, and kagome, on a superconducting thin film.

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Section: Selectionmentioning

confidence: 99%

Precision Modeling: Application of Metaheuristics on Current–Voltage Curves of Superconducting Films

et al. 2018

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“…Thus, the premature convergence (too much exploitation) and blind random search (too much exploration) phenomena can be avoided. The applied methods for adapting the crossover and mutation probabilities are presented in McGinley et al (2011). The two probabilities vary in pre-defined ranges based on the standard population diversity (SPD) describing a population's solution space diversity.…”

Section: Archived Ga Algorithmmentioning

confidence: 99%

“…As introduced in subsubsection 3.1.2, both crossover and mutation probabilities are adaptive. The same ranges of the probabilities, suggested by McGinley et al (2011), are set for the two optimizers for comparison purpose. The size of population in each generation is set to 30 for OGA, while a relatively small size of population (i.e., 6) is set for AGA.…”

Section: Performance Of Agamentioning

confidence: 99%

A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation

Jin

Kosonen

2017

Advances in Engineering Software

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A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation. Advances in Engineering SoftwareAccess to the published version may require subscription. N.B. When citing this work, cite the original published paper. AbstractTraffic flow is considered as a stochastic process in road traffic modeling. Computer simulation is a widely used tool to represent traffic system in engineering applications. The increased traffic congestion in urban areas and their impacts require more efficient controls and management. While the effectiveness of control schemes highly depends on accurate traffic model and appropriate control settings, optimization techniques play a central role for determining the control parameters in traffic planning and management applications. However, there is still a lack of research effort on the scientific computing framework for optimizing traffic control and operations and facilitating real planning and management applications. To this end, the present study proposes a model-based optimization framework to integrate essential components for solving road traffic control problems in general. In particular, the framework is based on traffic simulation models, while the solution needs extensive computation during the engineering optimization process. In this study, an advanced genetic algorithm, extended by an external archive for storing globally elite genes, governs the computing framework, and it shows superior performance than the ordinary genetic algorithm because of the reduced number of fitness function evaluations in engineering applications. To evaluate the optimization algorithm and validate the whole software framework, this paper illustrates a detailed application for optimization of traffic light controls. The study optimizes a simple road network of two intersections in Stockholm to demonstrate the model-based optimization processes as well as to evaluate the presented algorithm and software performance.

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“…In the adaptive GA process to minimize (8), the crossover and mutation probabilities are adaptively changed depending on the population dispersion [12] [13]. For example, if the dispersion is too high, the crossover probability is increased and the mutation probability is decreased and vice versa.…”

Section: Generation Of Equivalent Circuitmentioning

confidence: 99%

Equivalent circuit of antennas generated by model order reduction

Sato

Mori

Shimotani

et al. 2016

JAE

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Abstract. It is known that the proper orthogonal decomposition (POD), which is one of the effective approaches for model order reduction (MOR), can effectively reduce computational time for finite element method (FEM). This paper proposes a new method for generation of equivalent circuits based on POD-based FEM. In this method, the circuit parameters are determined by genetic algorithm so that the input impedance of the equivalent circuit is coincident with that computed by POD-based finite element method (FEM). For test of the proposed method, POD-based FEM is applied to analysis of a dipole antenna which is loaded with the Cockcroft Walton circuit. It is shown that this method can reduce the computational time to generate the equivalent circuit without deterioration of accuracy.

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Maintaining Healthy Population Diversity Using Adaptive Crossover, Mutation, and Selection

Cited by 116 publications

References 37 publications

Precision Modeling: Application of Metaheuristics on Current–Voltage Curves of Superconducting Films

Precision Modeling: Application of Metaheuristics on Current–Voltage Curves of Superconducting Films

A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation

Equivalent circuit of antennas generated by model order reduction

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