Genetic Programming Approach Research Articles

An efficient approach for feature construction of high-dimensional microarray data by random projections.

Dimensionality reduction of microarray data is a very challenging task due to high computational time and the large amount of memory required to train and test a model. Genetic programming (GP) is a stochastic approach to solving a problem. For high dimensional datasets, GP does not perform as well as other machine learning algorithms. To explore the inherent property of GP to generalize models from low dimensional data, we need to consider dimensionality reduction approaches. Random projections (RPs) have gained attention for reducing the dimensionality of data with reduced computational cost, compared to other dimensionality reduction approaches. We report that the features constructed from RPs perform extremely well when combined with a GP approach. We used eight datasets out of which seven have not been reported as being used in any machine learning research before. We have also compared our results by using the same full and constructed features for decision trees, random forest, naive Bayes, support vector machines and k-nearest neighbor methods.

A Multigene Genetic Programming approach for modeling effect of particle size in a liquid–solid circulating fluidized bed reactor

This communication presents the application of Multigene Genetic Programming, a new soft computing technique to investigate the effects of particle size on hydrodynamics behavior of a liquid–solid circulating fluidized bed (LSCFB) riser. The Multigene Genetic Programming based model is developed/trained based on experimental data collected from a pilot scale LSCFB reactor using two different size glass beads (500 & 1200μm) as solid phase and water as liquid phase. The trained Genetic Programming model successfully predicted experimental phase holdups of the LSCFB riser under different operating parameters. It is observed that the model predicted cross-sectional average of solids holdups in the axial directions and radial flow structure are well agreement with the experimental values. The statistical performance indicators including the mean absolute error (∼5.89%) and the correlation coefficient (∼0.982) also show favorable indications of the suitability of Genetic Programming modeling approach in predicting the solids holdup of the LSCFB system.

Genetic programming for soil-fiber composite assessment

Unconfined compressive strength (UCS) of soil is one of the basic index parameters for representing the compressive bearing strength of soil. Fiber reinforced soil is one of the most popular and practical ground improvement approaches used in geotechnical infrastructures. Analytical models for estimating UCS of soil-fiber composites have been developed in the literature. However, these models rarely incorporate the combined effects of dynamic field parameters such as fiber content, soil moisture, and density. These effects can be studied by the development of a holistic model based on a dimensionless strength improvement factor (SIF), which is defined as the ratio of UCS of reinforced soil to the unreinforced UCS. The current model estimating SIF indicates the improvement expected in UCS of soil-PP fiber composite based on the three design conditions such as fiber content, soil density, and moisture content. For this purpose, a series of 108 laboratory tests were first conducted to measure UCS of both fiber-reinforced soil and unreinforced soil under different fiber contents, soil density, and soil moisture content. Clayey silt soil and commercially used polypropylene (PP) fibers were selected in this study as soil and fiber material respectively. Genetic programming (GP) approach was then used to formulate models based on the measured data. The hidden non-linear relationships between SIF and the three inputs were determined by sensitivity and parametric analysis of the GP model. It was found that the moisture content in the soil has the highest influence on the strength factor that accounts for the change in strength. Coupled effects of soil parameters (soil moisture, soil density) and fiber content have been studied using parametric analysis which includes different possible field conditions (parameters). The results have been discussed along with the reinforcement mechanism of PP fiber for different soil conditions. It is believed that the robust GP model developed will be useful to determine optimum input values for designing safe bearing foundation soils which are reinforced with PP fibers.

Dynamic Generation of Internet of Things Organizational Structures Through Evolutionary Computing

In today’s world, intelligent embedded devices and sensors are interconnected into a dynamic and global network infrastructure is referred to as the Internet of Things (IoT). It has been widely recognized that the performance of an IoT is highly affected by how it is organized. A large-scale system may have billions of possible ways of being organized, which makes it impractical to find a high quality choice of organization by manual means. In this paper, we propose a genetic algorithm (GA) aided framework for generating hierarchical IoT organizational structures. We propose a novel unique mapping between organizational structures and genome representations. Since hierarchical (i.e., tree-structured) organizations are one of the most common forms of organizations, we propose a novel method to map the phenotypic hierarchical structure space into a genome-like array representation space. This new representation opens up opportunities for evolutionary computing techniques to help IoT applications automatically generate organizational structures according to desired objective functions. Based on this mapping, we introduce the hierarchical GA which enriches standard genetic programming approaches with the hierarchical crossover operator with a repair strategy and the mutation of small perturbation operator. The proposed approach is evaluated in an IoT-based information retrieval system. The results have shown that competitive baseline structures which lead to IoT organizations with good performance in terms of utility can be found by the proposed approach during the evolutionary search. Compared with the traditional genetic operators, the newly introduced operators produced organizations of higher utility more consistently under a variety of test cases. The proposed approach is computationally efficient in large search spaces and provides a novel method for future generations of IoT systems to autonomously improve performance.

Implementing the template method pattern in genetic programming for improved time series prediction

Modularity is an ongoing focus in genetic programming research. Enhanced modularity can accelerate solution convergence and increase human understanding and knowledge gained from evolved programs. Prior advances in modularity research have addressed programming language elements such as functions, modules, and recursion. This paper proposes improving modularity by considering non-language elements, specifically software design patterns. A new genetic programming technique implementing the template method pattern is described. This technique was tested and compared to existing genetic programming approaches in the prediction of nonlinear time series subject to abrupt changes in the underlying data generation process. Such series are often seen in areas such as finance and meteorology and have proved challenging for genetic programming to model and predict. Experimental results demonstrate the potential for incorporating additional software design patterns into genetic programming and applying these techniques to additional problem domains.

Multi-objective sizing and topology optimization of truss structures using genetic programming based on a new adaptive mutant operator

Most real-world engineering problems deal with multiple conflicting objectives simultaneously. In order to address this issue in truss optimization, this paper presents a multi-objective genetic programming approach for sizing and topology optimization of trusses. It aims to find the optimal cross-sectional areas and connectivities between the nodes to achieve a set of trade-off solutions to satisfy all the optimization objective functions subjected to some constraints such as kinematic stability, maximum allowable stress in members and nodal deflections. It also uses the variable-length representation of potential solutions in the shape of computer programs and evolves to the potential final set of solutions. This approach also employs an adaptive mutant factor besides the classical genetic operators to improve the exploring capabilities of Genetic Programming in structural optimization. The intrinsic features of genetic programming help to identify redundant truss members and nodes in the design space, while no violation of constraints occurs. Our approach applied to some numerical examples and found a better non-dominated solution set in the most cases in comparison with the competent methods available in the literature.

Modeling and prediction of metallic powder behavior in explosive compaction process by using genetic programming method based on dimensionless numbers

Explosive compaction process of metallic powders has been studied using a semi-empirical method. This method utilizes dimensional analysis along with genetic programming approach to obtain an expression relating the final density of compacts to the effective parameters during compaction process such as shock compaction energy, properties of metallic powder, and geometry of the problem and explosive charge. Dimensionless numbers have been constructed based on the effective parameters using a complete set of input–output experimental data. The obtained dimensionless numbers then have been applied as input–output data pairs for genetic programming optimization process considering modeling error as the objective. The obtained results show that the proposed model using dimensional analysis method along with genetic programming can predict the final density of compacts with 99.8% accuracy. Also, the outputs of the proposed model have been compared with those obtained by group method of data handling type neural network in the literature. Consequently, genetic programming method has much less root mean square error than group method of data handling model and can be successfully used for modeling and prediction of the complex process behavior.

Design and analysis of capacity models for Lithium-ion battery

Past studies on battery models is focussed on formulation of physics-based models, empirical models and fusion models derived from the battery pack data of electric vehicle. It is desirable to have an explicit, robust and accurate models for battery states estimation in-order to ensure its proper reliability and safety. The present work conducts a brief survey on battery models and will propose the evolutionary approach of Genetic programming (GP) for the battery capacity estimation. The experimental design for GP simulation comprises of the inputs such as the battery temperature and the rate of discharge. Further, the seven objective functions in GP approach is designed by introducing the complexity based on the order of polynomial. This step will ensure the precise functions evaluation in GP and drives the evolutionary search towards its optimum solutions. The design and analysis of the GP based battery capacity models involves the statistical validation of the seven objective functions based on error metrics with 2-D and 3-D surface plots. The results conclude that the GP models using Structural risk minimization (SRM) objective function accurately estimate the battery capacity based on the variations of the inputs. 2-D and 3-D surface analysis of the GP model reveals the increasing–decreasing nature of temperature-battery capacity curve with temperature the dominant input. The battery capacity model obtained using SRM as an objective function in GP is robust and thus can be integrated in the electric vehicle system for monitoring its performance and ensure its safety.

Identifying models of dielectric breakdown strength from high-throughput data via genetic programming

The identification of models capable of rapidly predicting material properties enables rapid screening of large numbers of materials and facilitates the design of new materials. One of the leading challenges for computational researchers is determining the best ways to analyze large material data sets to identify models that can rapidly predict a given property. In this paper, we demonstrate the use of genetic programming to generate simple models of dielectric breakdown based on 82 representative dielectric materials. We identified the band gap Eg and phonon cut-off frequency ωmax as the two most relevant features, and new classes of models featuring functions of Eg and ωmax were uncovered. The genetic programming approach was found to outperform other approaches for generating models, and we discuss some of the advantages of this approach.

Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-Invariant Texture Image Descriptors

The goodness of the features extracted from the instances and the number of training instances are two key components in machine learning, and building an effective model is largely affected by these two factors. Acquiring a large number of training instances is very expensive in some situations such as in the medical domain. Designing a good feature set, on the other hand, is very hard and often requires domain expertise. In computer vision, image descriptors have emerged to automate feature detection and extraction; however, domain-expert intervention is typically needed to develop these descriptors. The aim of this paper is to utilize genetic programming to automatically construct a rotation-invariant image descriptor by synthesizing a set of formulas using simple arithmetic operators and first-order statistics, and determining the length of the feature vector simultaneously using only two instances per class. Using seven texture classification image datasets, the performance of the proposed method is evaluated and compared against eight domain-expert hand-crafted image descriptors. Quantitatively, the proposed method has significantly outperformed, or achieved comparable performance to, the competitor methods. Qualitatively, the analysis shows that the descriptors evolved by the proposed method can be interpreted.

Robust model for optimization of forming process for metallic bipolar plates of cleaner energy production system

Energy production systems such as proton-exchange membrane fuel cell (PEMFC) has a promising future in the cleaner energy market due to zero emissions. Rubber pad forming (RPF) process of metallic bipolar plates of PEMFCs is gaining attention among the researchers. Studies based on design of experiments have been conducted to find the crucial parameters of the forming process. These methods are based on the assumptions of the model structure, correlated residuals, etc., which can cause uncertainty in estimation ability of the model on unseen data. Therefore, the present study focuses on the design of robust models of these parameters for PEMFCs using an optimization approach of genetic programming (GP). The inputs from the experiments considered in GP are radius, the friction coefficient, the filling factor and the minimum thickness. Experiments on PEMFCs validates the performance of the GP models. Further, the relationships between the two inputs and the three outputs for PEMFCs are generated as well as the contributions of each input to each of the output. Optimization of the models generated by GP can further determine the forming quality of metallic bipolar plates of PEMFCs by an appropriate setting of the two inputs.

Comparison of a genetic programming approach with ANFIS for power amplifier behavioral modeling and FPGA implementation

Accurate modeling of power amplifiers (PA) is of upmost importance in the design process of wireless communication systems where a high linearity and efficiency is required. To deal with the nonlinear behavior of PAs effectively a linearization stage is applied to minimize the distortions of in-band and adjacent transmission channels, which translate to an improvement of the signal integrity and the operation cost of the transmitter system. This paper presents a method based on genetic programming with a local search heuristic (GP-LS) to emulate the electrical memory effects by using the characteristic conversion curves of the radio frequency (RF) PA NXP Semiconductor of 10 W GaN HEMT working at 2.34 GHz. This method is compared with an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) through several performance metrics (NMSE, MAE and correlation coefficient), with GP-LS achieving a better modeling accuracy. Moreover, the models produced by GP-LS permit a reduction in the required hardware resources, when it is implemented on a Field-Programmable Gate Array through the DSP Builder tool. The models are derived using a data-driven approach, posed in two different ways. Firstly, experiments are performed using a testbed Arria V GX for a flexible vector signal generation that provides the raw data of the PA characterization using an LTE-Advanced signal with 10-MHz bandwidth. Secondly, the modeling is derived from a filtered version of the data and then adding a high-frequency signal as a post processing step to approximate the true behavior of the system. In both cases, the models are generated with ANFIS and GP-LS, performing extensive logic-based simulations and implementing the models on a Cyclone III development board. Both approaches are compared based on accuracy and required hardware resources, with GP-LS substantially outperforming ANFIS. These results suggest that the GP-LS models can be implemented in a digital predistortion chain and used in the linearization stage for a RF-PA.

Comparison of multi-gene genetic programming and dynamic evolving neural-fuzzy inference system in modeling pan evaporation

AbstractAccurately modeling pan evaporation is important in water resources planning and management and also in environmental engineering. This study compares the accuracy of two new data-driven methods, multi-gene genetic programming (MGGP) approach and dynamic evolving neural-fuzzy inference system (DENFIS), in modeling monthly pan evaporation. The climatic data, namely, minimum temperature, maximum temperature, solar radiation, relative humidity, wind speed, and pan evaporation, obtained from Antakya and Antalya stations, Mediterranean Region of Turkey were utilized in the study. The MGGP and DENFIS methods were also compared with genetic programming (GP) and calibrated version of Hargreaves Samani (CHS) empirical method. For Antakya station, GP had slightly better accuracy than the MGGP and DENFIS models and all the data-driven models performed were superior to the CHS while the DENFIS provided better performance than the other models in modeling pan evaporation at Antalya station. The effect of periodicity input to the models' accuracy was also investigated and it was found that adding periodicity significantly increased the accuracy of MGGP and DENFIS models.

Development of genetic programming-based model for predicting oyster norovirus outbreak risks

Oyster norovirus outbreaks pose increasing risks to human health and seafood industry worldwide but exact causes of the outbreaks are rarely identified, making it highly unlikely to reduce the risks. This paper presents a genetic programming (GP) based approach to identifying the primary cause of oyster norovirus outbreaks and predicting oyster norovirus outbreaks in order to reduce the risks. In terms of the primary cause, it was found that oyster norovirus outbreaks were controlled by cumulative effects of antecedent environmental conditions characterized by low solar radiation, low water temperature, low gage height (the height of water above a gage datum), low salinity, heavy rainfall, and strong offshore wind. The six environmental variables were determined by using Random Forest (RF) and Binary Logistic Regression (BLR) methods within the framework of the GP approach. In terms of predicting norovirus outbreaks, a risk-based GP model was developed using the six environmental variables and various combinations of the variables with different time lags. The results of local and global sensitivity analyses showed that gage height, temperature, and solar radiation were by far the three most important environmental predictors for oyster norovirus outbreaks, though other variables were also important. Specifically, very low temperature and gage height significantly increased the risk of norovirus outbreaks while high solar radiation markedly reduced the risk, suggesting that low temperature and gage height were associated with the norovirus source while solar radiation was the primary sink of norovirus. The GP model was utilized to hindcast daily risks of oyster norovirus outbreaks along the Northern Gulf of Mexico coast. The daily hindcasting results indicated that the GP model was capable of hindcasting all historical oyster norovirus outbreaks from January 2002 to June 2014 in the Gulf of Mexico with only two false positive outbreaks for the 12.5-year period. The performance of the GP model was characterized with the area under the Receiver Operating Characteristic curve of 0.86, the true positive rate (sensitivity) of 78.53% and the true negative rate (specificity) of 88.82%, respectively, demonstrating the efficacy of the GP model. The findings and results offered new insights into the oyster norovirus outbreaks in terms of source, sink, cause, and predictors. The GP model provided an efficient and effective tool for predicting potential oyster norovirus outbreaks and implementing management interventions to prevent or at least reduce norovirus risks to both the human health and the seafood industry.

An application of evolutionary system identification algorithm in modelling of energy production system

The present work introduces the literature review on System Identification (SI) by classifying it into several fields. The review summarizes the need of evolutionary SI method that automates the model structure selection and its parameter evaluation based on only the system data. In this context, the evolutionary SI approach of genetic programming (GP) is applied in modeling and optimization of cleaner energy system such as direct methanol fuel cell. The functional response of the power density of the fuel cell with respect to input conditions is selected based on the minimum training error. Further, an experimental data is used to validate the robustness of the formulated GP model. The analysis based on 2-D and 3-D parametric procedure is further conducted to reveals insights into functioning of the fuel cell. The pareto front obtained from optimization of model reveals that the operating temperature of 64.5°C, methanol flow rate of 28.04mL/min and methanol concentration of 0.29M are the optimum settings for achieving the maximum power density of 7.36mW/cm2 for DMFC.

A Genetic Programming approach for feature selection in highly dimensional skewed data

High dimensionality, also known as the curse of dimensionality, is still a major challenge for automatic classification solutions. Accordingly, several feature selection (FS) strategies have been proposed for dimensionality reduction over the years. However, they potentially perform poorly in face of unbalanced data. In this work, we propose a novel feature selection strategy based on Genetic Programming, which is resilient to data skewness issues, in other words, it works well with both, balanced and unbalanced data. The proposed strategy aims at combining the most discriminative feature sets selected by distinct feature selection metrics in order to obtain a more effective and impartial set of the most discriminative features, departing from the hypothesis that distinct feature selection metrics produce different (and potentially complementary) feature space projections. We evaluated our proposal in biological and textual datasets. Our experimental results show that our proposed solution not only increases the efficiency of the learning process, reducing up to 83% the size of the data space, but also significantly increases its effectiveness in some scenarios.

Water Curtain System Pre-design for Crude Oil Storage URCs: A Numerical Modeling and Genetic Programming Approach

In this paper the main criteria of the water curtain system for unlined rock caverns (URCs) is described. By the application of numerical modeling and genetic programming (GP), a method for water curtain system pre-design for Iranian crude oil storage URCs (common dimension worldwide) is presented. A comprehensive set of numerical simulations is performed using the finite element based commercial software (COMSOL 5.1) where the results are used as database for genetic programming. Describing equations of water inflow to the filled and empty caverns and water production rate of water curtain boreholes are generated using GP. By equating the proposed equations to each other, water curtain system can be pre-designed. Relative error of the generated GP equations shows their ability and accuracy. Applying a standard regression coefficient method, sensitivity analysis of parameters related to water curtain performance and water inflow to the caverns is performed as well. The results help the design of the water curtain system for crude oil storage caverns worldwide.

Highly effective Security Techniques in OSN Based on Genetic Programming Approach

Assurance is one of the grinding centers that enhances when trades get mediated in Online Community Techniques (Online Social Networks). Diverse gatherings of utilization innovation specialists have limited the 'OSN security issue' as one of surveillance, institutional or open security. In taking care of these issues they have moreover overseen them just as they were person. We adapt that the elite security issues are caught and that evaluation on genuine feelings of serenity in Online Social Networks would advantage from a more exhaustive method. Nowadays, points of interest systems mean a critical piece of relationship; by losing security, these organizations will decrease a ton of pleasant areas to see as well. The inside motivation behind subtle elements security (Information Security) is risk control. There are a great deal of discovering works and exercises in security danger control (ISRM, for example, NIST 800-30 and ISO/IEC 27005. Regardless, only few works of appraisal focus on Information Security danger diminishment, while the signs depict normal determinations and suggestions. They don't give any use ideas concerning ISRM; truth be told diminishing the Information Security dangers in questionable conditions is cautious. Subsequently, this papers joined an acquired counts (GA) for Information Security danger loss of weaknesses. Finally, the parity of the associated system was broke down through a reflection.

An investigation of dynamic fitness measures for genetic programming

This research investigates the hypothesis that the use of different fitness measures at the different generations of genetic programming (GP) is more effective than the convention of applying the same fitness measure individually throughout GP. A genetic algorithm (GA) is used to induce the sequence in which fitness measures should be applied over the GP generations. Subsequently, the performance of a GP system applying the evolved fitness measure sequence is compared with the conventional GP approach. The former approach is shown to significantly outperform standard GP on varied benchmark problems. Furthermore, the evolved fitness measure sequences are shown to generalize within a problem class: therefore, the sequences can be evolved off-line for different problem classes. Critically, sequences trained on the problem classes are also shown to generalize to complex, real-world problems. Overall, the findings of the study are in favor of the hypothesis. This study has revealed the effectiveness of dynamic fitness measures when applied to benchmark and real-world problems.

Roadheader performance prediction using genetic programming (GP) and gene expression programming (GEP) techniques

Roadheading machines play a vital role in excavation operation in tunneling and mining industries notably when selective mining is required. Roadheaders are more effective in soft to medium rock formations due to a higher cutting rate in such strata. A precise prediction of machine’s performance is a crucial issue, as it has considerable effects on excavation planning, project’s cost estimation, machine specification selection as well as safety of the project. In this research, a database of machine performance and some geomechanical parameters of rock formations from Tabas coal mine project, the largest and fully mechanized coal mine in Iran, has been established, including instantaneous cutting rate (ICR), uniaxial compressive strength, Brazilian tensile strength, rock quality designation, influence of discontinuity orientation (Alpha angle) and specific energy. Afterward, the parameters were analyzed through genetic programming (GP) and gene expression programming (GEP) approaches to yield more accurate models to predict the performance of roadheaders. As statistical indices, coefficient of determination, root mean square error and variance account were used to evaluate the efficiency of the developed models. According to the obtained results, it was observed that developed models can effectively be implemented for prediction of roadheader performance. Moreover, it was concluded that performance of the GEP model is better than the GP model. A high conformity was observed between predicted and measured roadheader ICR for GEP model.