Linear Genetic Programming Approach Research Articles

A Linear Genetic Programming Approach for Modelling Electricity Demand Prediction in Victoria

A Linear Genetic Programming Approach for Modelling Electricity Demand Prediction in Victoria

A Genetic Programming Based Heuristic to Simplify Rugged Landscapes Exploration

Some optimization problems are difficult to solve due to a considerable number of local optima, which may result in premature convergence of the optimization process. To address this problem, we propose a novel heuristic method for constructing a smooth surrogate model of the original function. The surrogate function is easier to optimize but maintains a fundamental property of the original rugged fitness landscape: the location of the global optimum. To create such a surrogate model, we consider a linear genetic programming approach coupled with a self-tuning fitness function. More specifically, to evaluate the fitness of the produced surrogate functions, we employ Fuzzy Self-Tuning Particle Swarm Optimization, a setting-free version of particle swarm optimization. To assess the performance of the proposed method, we considered a set of benchmark functions characterized by high noise and ruggedness. Moreover, the method is evaluated over different problems’ dimensionalities. The proposed approach reveals its suitability for performing the proposed task. In particular, experimental results confirm its capability to find the global argminimum for all the considered benchmark problems and all the domain dimensions taken into account, thus providing an innovative and promising strategy for dealing with challenging optimization problems. Doi: 10.28991/ESJ-2023-07-04-01 Full Text: PDF

Numerical analysis of the shear strength of circular reinforced concrete columns subjected to cyclic lateral loads using linear genetic programming

PurposeThe shear strength of reinforced concrete (RC) columns under cyclic lateral loading is a crucial concern, particularly, in the seismic design of RC structures. Considering the costly procedure of testing methods for measuring the real value of the shear strength factor and the existence of several parameters impacting the system behavior, numerical modeling techniques have been very much appreciated by engineers and researchers. This study aims to propose a new model for estimation of the shear strength of cyclically loaded circular RC columns through a robust computational intelligence approach, namely, linear genetic programming (LGP).Design/methodology/approachLGP is a data-driven self-adaptive algorithm recently used for classification, pattern recognition and numerical modeling of engineering problems. A reliable database consisting of 64 experimental data is collected for the development of shear strength LGP models here. The obtained models are evaluated from both engineering and accuracy perspectives by means of several indicators and supplementary studies and the optimal model is presented for further purposes. Additionally, the capability of LGP is examined to be used as an alternative approach for the numerical analysis of engineering problems.FindingsA new predictive model is proposed for the estimation of the shear strength of cyclically loaded circular RC columns using the LGP approach. To demonstrate the capability of the proposed model, the analysis results are compared to those obtained by some well-known models recommended in the existing literature. The results confirm the potential of the LGP approach for numerical analysis of engineering problems in addition to the fact that the obtained LGP model outperforms existing models in estimation and predictability.Originality/valueThis paper mainly represents the capability of the LGP approach as a robust alternative approach among existing analytical and numerical methods for modeling and analysis of relevant engineering approximation and estimation problems. The authors are confident that the shear strength model proposed can be used for design and pre-design aims. The authors also declare that they have no conflict of interest.

Prediction of scour depth at piers with debris accumulation effects using linear genetic programming

Exact evaluation of scour depth around piers under debris accumulation is crucial for the safe design of pier structures. Experimental studies on scouring around pier bridges with debris accumulation have been conducted to estimate the maximum scour depth using various empirical relationships. However, due to the oversimplification of a complex process, the proposed relationships have not always been able to accurately predict the pier scour depth. This research proposes linear genetic programming (LGP) approach as an extension of the genetic programming to predict the scour depth around bridge piers. Among the artificial intelligence techniques, LGP and locally weighted linear regression (LWLR) models have not been used to predict the scour depth at bridge piers. Literature experimental data were collected and used to develop the models. The performance of the LGP method was compared with gene-expression programming, LWLR, multilinear regression and empirical equations using rigorous statistical criteria. The correlation coefficient (R) and the root mean squared error (RMSE) were (R = 0.962, RMSE =0.31) and (R = 0.885, RMSE =0.542) for the LGP and LWLR, respectively. The results demonstrated the superiority of the LGP method for increasing the accuracy of the predicted scour depth in comparison with the other models.

Towards application of linear genetic programming for indirect estimation of the resilient modulus of pavements subgrade soils

The success of a flexible pavement design depends on the accuracy of determining the structural response of the pavement to dynamic loads, known as resilient modulus (MR). This paper explores the potential of a branch of the computational intelligence techniques, namely linear genetic programming (LGP), for indirect estimation of the MR of pavements subgrade soils. Furthermore, a pre-design model is proposed which characterises MR factor in terms of subgrade soil properties and applied stress states utilising a selected database which comprises of several test results conducted on cohesive Ohio A-6 soils. In order to assess the degree of accuracy and reliability of the obtained model, various statistical criteria and verification study phases are conducted. Finally, better results of the obtained model in comparison with traditional models prove the robustness and capability of LGP approach for indirect estimation of MR of pavement subgrade soils.

An Ensemble Empirical Mode Decomposition, Self-Organizing Map, and Linear Genetic Programming Approach for Forecasting River Streamflow

This study focused on employing Linear Genetic Programming (LGP), Ensemble Empirical Mode Decomposition (EEMD), and the Self-Organizing Map (SOM) in modeling the rainfall–runoff relationship in a mid-size catchment. Models were assessed with regard to their ability to capture daily discharge at Lock and Dam 10 along the Kentucky River as well as the hybrid design of EEM-SOM-LGP to make predictions multiple time-steps ahead. Different model designs were implemented to demonstrate the improvements of hybrid designs compared to LGP as a standalone application. Additionally, LGP was utilized to gain a better understanding of the catchment in question and to assess its ability to capture different aspects of the flow hydrograph. As a standalone application, LGP was able to outperform published Artificial Neural Network (ANN) results over the same dataset, posting an average absolute relative error (AARE) of 17.118 and Nash-Sutcliff (E) of 0.937. Utilizing EEMD derived IMF runoff subcomponents for forecasting daily discharge resulted in an AARE of 14.232 and E of 0.981. Clustering the EEMD-derived input space through an SOM before LGP application returned the strongest results, posting an AARE of 10.122 and E of 0.987. Applying LGP to the distinctive low and high flow seasons demonstrated a loss in correlation for the low flow season with an under-predictive nature signified by a normalized mean biased error (NMBE) of −2.353. Separating the rising and falling trends of the hydrograph showed that the falling trends were more easily captured with an AARE of 8.511 and E of 0.968 compared to the rising trends AARE of 38.744 and E of 0.948. Utilizing the EEMD-SOM-LGP design to make predictions multiple-time-steps ahead resulted in a AARE of 43.365 and E of 0.902 for predicting streamflow three days ahead. The results demonstrate the effectiveness of utilizing EEMD and an SOM in conjunction with LGP for streamflow forecasting.

Rectangular side weirs discharge coefficient estimation in circular channels using linear genetic programming approach

Side weirs are diversion structures extensively used in irrigation, flood protection and combined sewer systems. Accurate estimation of the discharge coefficient (Cd) of side weirs is essential to compute the water surface profile over the weirs and to determine the lateral outflow rate from the system. In this paper, we have utilized a linear genetic programming (LGP) technique to develop new empirical formulas for the estimation of Cd of sharp-edged rectangular side weirs located in circular channels. For this aim, we have employed a total of 1,686 laboratory experimental observations in both sub- and supercritical flow regimes in order to train and validate the proposed models. The performance of the LGP-based models was also compared with those of different multilinear and nonlinear regression models in terms of root mean squared errors, mean absolute errors, and determination coefficient. The results indicated that an explicit LGP-based model using only mathematical functions could be employed successfully in Cd estimation in both sub- and supercritical flow conditions. Genetic-based sensitivity analysis among the input parameters demonstrated that Froude number at upstream of the weir has the most impact on the Cd estimation.

Erratum to: A linear genetic programming approach for the prediction of solar global radiation

Erratum to: A linear genetic programming approach for the prediction of solar global radiation

A linear genetic programming approach for the prediction of solar global radiation

In this article, the linear genetic programming (LGP) is utilized to predict the solar global radiation. The solar radiation is formulated in terms of several climatological and meteorological parameters. Comprehensive databases containing monthly data collected for 6 years (1995–2000) in two nominal cities in Iran are used to develop LGP-based models. Separate models are established for each city. To verify the performance of the proposed models, they are applied to estimate the solar global radiation of test data of database. The contribution of the parameters affecting the solar radiation is evaluated through a sensitivity analysis. The results indicate that the LGP models give precise estimations of the solar global radiation and significantly outperform traditional angstrom’s model.

Nonlinear modeling of soil deformation modulus through LGP-based interpretation of pressuremeter test results

Soil deformation modulus is an essential parameter for the analysis of behavior of substructures. Despite its importance, little attention is paid to developing empirical models for predicting the deformation moduli obtained from the field tests. To cope with this issue, this paper presents the development of a new prediction model for the pressuremeter soil deformation modulus utilizing a linear genetic programming (LGP) methodology. The LGP model relates the soil secant modulus obtained from the pressuremeter tests to the soil index properties. The best model was selected after developing and controlling several models with different combinations of the influencing parameters. The experimental database used for developing the models was established upon several pressuremeter tests conducted on different soil types at depths of 3–40m. To verify the applicability of the derived model, it was employed to estimate the soil moduli of portions of test results that were not included in the analysis. Further, the generalization capability of the model was verified via several statistical criteria. The sensitivity of the soil deformation modulus to the influencing variables was examined and discussed. Moisture content and soil dry unit weight were found to be efficient representatives of the initial state and consolidation history of the soil for determining its deformation modulus. The results indicate that the LGP approach accurately characterizes the soil deformation modulus leading to a very good prediction performance. The correlation coefficients between the experimental and predicted soil modulus values are equal to 0.908 and 0.901 for the calibration and testing data sets, respectively.

New linear genetic programming approach

A new genetic programming named Symbol Genetic Programming(SGP) based on a new encoding method was proposed.This new encoding method absorbed the merits of many other linear genetic programming methods.It coded with a simple,unrestrained string.Based on its characteristic,multi-expressions could be contained in one individual without the increase of computation task.This method is proved to be effective and stable through the complexity analysis and experiment.

Extending the boundaries of design optimization by integrating fast optimization techniques with machine-code-based, linear genetic programming

Optimized models of complex physical systems are difficult to create and time consuming to optimize. The physical and business processes are often not well understood and are therefore difficult to model. The models of often too complex to be well optimized with available computational resources. Too often approximate, less than optimal models result. This work presents an approach to this problem that blends three well-tested components. First: We apply Linear Genetic Programming (LGP) to those portions of the system that are not well understood––for example, modeling data sets, such the control settings for industrial or chemical processes, geotechnical property prediction or UXO detection. LGP builds models inductively from known data about the physical system. The LGP approach we highlight is extremely fast and builds rapid to execute, high-precision models of a wide range of physical systems. Yet it requires few parameter adjustments and is very robust against overfitting. Second: We simulate those portions of the system––for example, the cost model for the processes––these are well understood with human built models. Finally: We optimize the resulting meta-model using Evolution Strategies (ES). ES is a fast, general-purpose optimizer that requires little pre-existing domain knowledge. We have developed this approach over a several years period and present results and examples that highlight where this approach can greatly improve the development and optimization of complex physical systems.