Application of genetic programming for modelling of material characteristics

Gusel, Leo; Brezočnik, Miran

doi:10.1016/j.eswa.2011.05.045

Cited by 20 publications

(9 citation statements)

References 12 publications

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“…In recent times, GP [36][37][38][39][40][41][42] is increasingly becoming a powerful alternate of ANNs [38-40, 43, 44], allowing the users to construct a meta-model with mathematical or logical functions of their own choice, which would be difficult to obtain through the paradigm of neural nets with rather restricted environment of weights, layers, and transfer functions. In GP, the encoding is done as a tree structure and the typical evolutionary operators like crossover, selection, and mutation are performed on a population of trees generated on the basis of some user defined function and terminal sets.…”

Section: Methodsmentioning

confidence: 99%

“…In GP, the encoding is done as a tree structure and the typical evolutionary operators like crossover, selection, and mutation are performed on a population of trees generated on the basis of some user defined function and terminal sets. In conventional GP, trees often tend to grow unmanageably with marginal or no improvement in fitness leading to a problem known as bloat [36][37][38][39][40][41][42] in the GP parlance. Often, some rogue trees develop with certain branches, returning division by zero or other execution problems of similar nature, rendering the implementation of GP quite complicated.…”

Section: Methodsmentioning

confidence: 99%

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Evolutionary Design of Nickel-Based Superalloys Using Data-Driven Genetic Algorithms and Related Strategies

Jha

Pettersson

Dulikravich

et al. 2014

Materials and Manufacturing Processes

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Data-driven models were constructed for the mechanical properties of multi-component Ni-based superalloys, based on systematically planned, limited experimental data using a number of evolutionary approaches. Novel alloy design was carried out by optimizing two conflicting requirements of maximizing tensile stress and time-to-rupture using a genetic algorithm-based multi-objective optimization method. The procedure resulted in a number of optimized alloys having superior properties. The results were corroborated by a rigorous thermodynamic analysis and the alloys found were further classified in terms of their expected levels of hardenabilty, creep, and corrosion resistances along with the two original objectives that were optimized. A number of hitherto unknown alloys with potential superior properties in terms of all the attributes ultimately emerged through these analyses. This work is focused on providing the experimentalists with linear correlations among the design variables and between the design variables and the desired properties, non-linear correlations (qualitative) between the design variables and the desired properties, and a quantitative measure of the effect of design variables on the desired properties. Pareto-optimized predictions obtained from various data-driven approaches were screened for thermodynamic equilibrium. The results were further classified for additional properties.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Evolutionary Design of Nickel-Based Superalloys Using Data-Driven Genetic Algorithms and Related Strategies

Jha

Pettersson

Dulikravich

et al. 2014

Materials and Manufacturing Processes

View full text Add to dashboard Cite

show abstract

“…It is worth mentioning that while artificial neural networks and other advanced computational methods were used for modeling characteristics of materials, see, for example, (Gençoglu, M.T. & Cebeci, 2009;Gusel & Brezocnik, 2011;Hwang et al, 2010), this research is the first rigorous systematic analysis and comparison of difference methodologies in ground electrical resistivity studies. This paper provides practical guidelines and examples of modelling and investigating non-linear relationships in engineering applications.…”

Section: Introductionmentioning

confidence: 99%

Modeling of electrical resistivity of soil based on geotechnical properties

Alsharari¹,

Olenko

Abuel-Naga

2020

Expert Systems with Applications

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Determining the relationship between the electrical resistivity of soil and its geotechnical properties is an important engineering problem. This study aims to develop methodology for finding the best model that can be used to predict the electrical resistivity of soil, based on knowing its geotechnical properties. The research develops several linear models, three non-linear models, and three artificial neural network models (ANN). These models are applied to the experimental data set comprises 864 observations and five variables. The results show that there are significant exponential negative relationships between the electrical resistivity of soil and its geotechnical properties. The most accurate prediction values are obtained using the ANN model. The cross-validation analysis confirms the high precision of the selected predictive model. This research is the first rigorous systematic analysis and comparison of difference methodologies in ground electrical resistivity studies. It provides practical guidelines and examples of design, development and testing non-linear relationships in engineering intelligent systems and applications.

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“…Many other different applications of knowledge-based intelligent systems based on the deterministic and/or non-deterministic modelling have been developed within mechanical engineering field (see for example [8][9][10][11][12][13][14][15]). Although PSO is commonly used, optimization can also be successfully done by applying neural networks [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Modelling of a Turning Process Using the Gravitational Search Algorithm

Hrelja¹,

Klančnik²,

Balič³

et al. 2014

Int. j. simul. model.

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This paper proposes the modelling of a turning process using a gravitational search algorithm (GSA). GSA is an optimization algorithm based on Newton's law of universal gravitation and mass interactions. In order to sufficiently describe the turning process, at least three independent variables are required: cutting speed, feed-rate, and cutting depth. Independent variables have impacts on dependent variables, which were in our case cutting force, surface roughness, and tool-life. The values of independent and dependent variables obtained by measurements serve as a knowledge database for feeding the GSA optimization process. During our research the GSA was used for optimizing the numerical coefficients of predefined polynomial models for describing the observed output variables. The accuracies of the obtained prediction models were proved by means of a testing data set that was excluded from the training data. The research showed that the obtained results were comparable with the other optimization algorithms such as particle swarm optimization (PSO). However, the optimization time required for GSA optimization was, in certain cases, significantly shorter.

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Application of genetic programming for modelling of material characteristics

Cited by 20 publications

References 12 publications

Evolutionary Design of Nickel-Based Superalloys Using Data-Driven Genetic Algorithms and Related Strategies

Evolutionary Design of Nickel-Based Superalloys Using Data-Driven Genetic Algorithms and Related Strategies

Modeling of electrical resistivity of soil based on geotechnical properties

Modelling of a Turning Process Using the Gravitational Search Algorithm

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