Multi expression programming: a new approach to formulation of soil classification

Alavi, Amir H.; Gandomi, Amir H.; Sahab, Mohammad Ghasem; Gandomi, Mostafa

doi:10.1007/s00366-009-0140-7

Cited by 139 publications

(85 citation statements)

References 10 publications

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“…Several programs can be evolved in a single MEP. The algorithm first randomly creates a number of computer programs and then uses genetic operators such as mutation and crossover to find the optimal model [28]. An example of a program generated by MEP is as given below: 0: l 1: m 2: 90, 1 3: n 4: ?2, 3 5: o 6: /4, 5 7: 91, 6…”

Section: Multi-expression Programmingmentioning

confidence: 99%

“…n)/o. For k genes, the fitness of each program can be evaluated using the following equation [27,28]: …”

Section: Multi-expression Programmingmentioning

confidence: 99%

“…Oltean and Grosan [27] showed that the advantage of MEP over other modeling techniques. There are few applications of this powerful tool in civil engineering domain [28,29]. It is worth mentioning that MEP possesses obvious advantage over traditional statistical modeling techniques because it does not need a preliminary definition of the form of the existing relationships.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of prediction models for shear strength of SFRCB using a machine learning approach

Sarveghadi

Gandomi

Bolandi

et al. 2015

Neural Comput & Applic

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In this study, new design equations were derived for the assessment of shear resistance of steel fiberreinforced concrete beams (SFRCB) utilizing multi-expression programming (MEP). The superiority of MEP over conventional statistical techniques is due to its ability in modeling of mechanical behavior without a need to predefine the model structure. The MEP models were developed using a comprehensive database obtained through an extensive literature review. New criteria were checked to verify the validity of the models. A sensitivity analysis was carried out and discussed. The MEP models provide good estimations of the shear strength of SFRCB. The developed models significantly outperform several equations found in the literature.

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Section: Multi-expression Programmingmentioning

confidence: 99%

“…n)/o. For k genes, the fitness of each program can be evaluated using the following equation [27,28]: …”

Section: Multi-expression Programmingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of prediction models for shear strength of SFRCB using a machine learning approach

Sarveghadi

Gandomi

Bolandi

et al. 2015

Neural Comput & Applic

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“…Since the late 1970s, numerous energy-based procedures have been proposed for evaluating the liquefaction potential of soil deposits [34]. The use of energy concept is shown to be a logical step in the evolution of liquefaction evaluation of soils [11]. One of the most common methods to solve the liquefaction problem uses experimental data to develop empirical models that relate the variables in the system.…”

Section: Problem Iv: Soil Liquefactionmentioning

confidence: 99%

“…The main advantage of GP over the traditional statistical and ANN techniques is their ability to generate prediction equations without assuming prior form of the existing relationships. GP and its variants have been successfully applied to various kinds of geotechnical and earthquake engineering problems [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems

Gandomi

Alavi

2011

Neural Comput & Applic

Self Cite

170

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Complexity of analysis of geotechnical behavior is due to multivariable dependencies of soil and rock responses. In order to cope with this complex behavior, traditional forms of engineering design solutions are reasonably simplified. Incorporating simplifying assumptions into the development of the traditional methods may lead to very large errors. This paper presents an endeavor to exploit a robust multi-gene genetic programming (MGGP) method for the analysis of geotechnical and earthquake engineering systems. MGGP is a modified genetic programming approach for model structure selection combined with a classical technique for parameter estimation. To justify the abilities of MGGP, it is systematically employed to formulate the complex geotechnical engineering problems. Different classes of the problems analyzed include the assessment of (i) undrained lateral load capacity of piles, (ii) undrained side resistance alpha factor for drilled shafts, (iii) settlement around tunnels, and (iv) soil liquefaction. The validity of the derived models is tested for a part of test results beyond the training data domain. Numerical examples show the superb accuracy, efficiency, and great potential of MGGP. Contrary to artificial neural networks and many other soft computing tools, MGGP provides constitutive prediction equations. The MGG-based solutions are particularly valuable for pre-design practices.

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A Robust Data‐Mining Approach to Bankruptcy Prediction

Divsalar

Roodsaz

Vahdatinia

et al. 2011

Journal of Forecasting

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In this study, new variants of genetic programming (GP), namely gene expression programming (GEP) and multi-expression programming (MEP), are utilized to build models for bankruptcy prediction. Generalized relationships are obtained to classify samples of 136 bankrupt and non-bankrupt Iranian corporations based on their financial ratios. An important contribution of this paper is to identify the effective predictive financial ratios on the basis of an extensive bankruptcy prediction literature review and upon a sequential feature selection analysis. The predictive performance of the GEP and MEP forecasting methods is compared with the performance of traditional statistical methods and a generalized regression neural network. The proposed GEP and MEP models are effectively capable of classifying bankrupt and non-bankrupt firms and outperform the models developed using other methods.

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Multi expression programming: a new approach to formulation of soil classification

Cited by 139 publications

References 10 publications

Development of prediction models for shear strength of SFRCB using a machine learning approach

Development of prediction models for shear strength of SFRCB using a machine learning approach

A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems

A Robust Data‐Mining Approach to Bankruptcy Prediction

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