Application Of Genetic Programming Research Articles

Symbolic regression based prediction of anisotropic closure in deep tunnels

This work investigates the applicability of Genetic Programming with Symbolic Regression to analyse the closure evolution of tunnels during and after excavation. Special attention is given to anisotropic closure evolution, which depends on the anisotropy of the initial stress state and the intrinsic anisotropy of the rock mass formation. A methodology is proposed that takes into account the information recorded during excavation to train the algorithm and find a free-form simple mathematical expression that captures the closure evolution over time. The proposed methodology is applied to two case studies of deep tunnels with high anisotropic convergence evolution. The results obtained show that this approach performs well with the small dataset used in this work. The proposed approach is an interesting tool to improve the understanding and prediction of the ground response.

Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables

Identifying patients who may develop severe COVID-19 has been of interest to clinical physicians since it facilitates personalized treatment and optimizes the allocation of medical resources. In this study, multi-gene genetic programming (MGGP), as an advanced artificial intelligence (AI) tool, was used to determine the importance of laboratory predictors in the prognosis of COVID-19 patients. The present retrospective study was conducted on 1455 patients with COVID-19 (727 males and 728 females), who were admitted to Allameh Behlool Gonabadi Hospital, Gonabad, Iran in 2020–2021. For each patient, the demographic characteristics, common laboratory tests at the time of admission, duration of hospitalization, admission to the intensive care unit (ICU), and mortality were collected through the electronic information system of the hospital. Then, the data were normalized and randomly divided into training and test data. Furthermore, mathematical prediction models were developed by MGGP for each gender. Finally, a sensitivity analysis was performed to determine the significance of input parameters on the COVID-19 prognosis. Based on the achieved results, MGGP is able to predict the mortality of COVID-19 patients with an accuracy of 60–92%, the duration of hospital stay with an accuracy of 53–65%, and admission to the ICU with an accuracy of 76–91%, using common hematological tests at the time of admission. Also, sensitivity analysis indicated that blood urea nitrogen (BUN) and aspartate aminotransferase (AST) play key roles in the prognosis of COVID-19 patients. AI techniques, such as MGGP, can be used in the triage and prognosis prediction of COVID-19 patients. In addition, due to the sensitivity of BUN and AST in the estimation models, further studies on the role of the mentioned parameters in the pathophysiology of COVID-19 are recommended.

Population Dynamics in Genetic Programming for Dynamic Symbolic Regression

This paper investigates the application of genetic programming (GP) for dynamic symbolic regression (SR), addressing the challenge of adapting machine learning models to evolving data in practical applications. Benchmark instances with changing underlying functions over time are defined to assess the performance of a genetic algorithm (GA) as a traditional evolutionary algorithm and an age-layered population structure (ALPS) as an open-ended evolutionary algorithm for dynamic symbolic regression. This study analyzes population dynamics by examining variable frequencies and impact changes over time in response to dynamic shifts in the training data. The results demonstrate the effectiveness of both the GA and ALPS in handling changing data, showcasing their ability to recover and evolve improved solutions after an initial drop in population quality following data changes. Population dynamics reveal that variable impacts respond rapidly to data changes, while variable frequencies shift gradually across generations, aligning with the indirect measure of fitness represented by variable impacts. Notably, the GA shows a strong dependence on mutation to avoid variables becoming permanently extinct, contrasting with the ALPS’s unexpected insensitivity to mutation rates owing to its reseeding mechanism for effective variable reintroduction.

Heuristic Ensemble Construction Methods of Automatically Designed Dispatching Rules for the Unrelated Machines Environment

Dynamic scheduling represents an important class of combinatorial optimisation problems that are usually solved with simple heuristics, the so-called dispatching rules (DRs). Designing efficient DRs is a tedious task, which is why it has been automated through the application of genetic programming (GP). Various approaches have been used to improve the results of automatically generated DRs, with ensemble learning being one of the best-known. The goal of ensemble learning is to create sets of automatically designed DRs that perform better together. One of the main problems in ensemble learning is the selection of DRs to form the ensemble. To this end, various ensemble construction methods have been proposed over the years. However, these methods are quite computationally intensive and require a lot of computation time to obtain good ensembles. Therefore, in this study, we propose several simple heuristic ensemble construction methods that can be used to construct ensembles quite efficiently and without the need to evaluate their performance. The proposed methods construct the ensembles solely based on certain properties of the individual DRs used for their construction. The experimental study shows that some of the proposed heuristic construction methods perform better than more complex state-of-the-art approaches for constructing ensembles.

Genetic Programming in Software Engineering: A Systematic Literature Review

Software Engineering is present in all the Software Development Life Cycle phases, implying a systematic and disciplined development process. Nowadays, there are optimization problems within the phases and activities of Software Engineering, problems in which a Genetic Programming (GP) approach can give better results due to its representation of individuals. This Systematic Literature Review aims to analyze the current state of the application of GP in Software Engineering by collecting the phases and activities of software development where Genetic Programming has been used and summarizing the advantages of using this technique. The results show that GP has been used in every phase of software development, although mainly in the construction phase. In addition, a promising area is Program Synthesis, which substantially impacts new fields such as Genetic Improvement.

Application of multigene genetic programming and water evaporation optimization technique for modeling and optimization of removal of heavy metals from ash pond water using cyanobacterial consortium

Abstract Heavy metals such as Lead(II), Nickel(II), Manganese(II), Cadmium(II), Chromium(VI), etc., are leached from the coal ash of thermal power plants. These metals contaminate ash pond water and subsequently contaminate groundwater. Phycoremediation using microalgae/cyanobacteria is an emerging technology for removal of heavy metals. The present study aims at phycoremediation of the said heavy metals from ash pond water using cyanobacterial consortium of Limnococcus limneticus and Leptolyngbya subtilis followed by the development of an accurate data-driven Multigene Genetic Programing (MGGP) approach for modeling and optimization of the process. The developed model was used to obtain a correlation between the average removal of metals and biomass production with all input factors such as the initial metal concentrations, pH, and days of incubation. To maximize metal removal and biomass production, the Water Evaporation Optimization (WEO) technique was applied to determine optimal values of input parameters. The application of WEO for the optimization of the phycoremediation process is the first of its kind and here lies the novelty of the study.

Designing relocation rules with genetic programming for the container relocation problem with multiple bays and container groups

The container relocation problem (CRP) is an NP-hard combinatorial optimisation problem that arises in yard management. The problem is concerned with loading all containers from the storage yard to the ship in a certain order. The yard layout consists of bays where containers are placed in stacks on top of each other, and each container has a due date that determines their retrieval order. Due to its complexity, heuristic methods are used to solve CRP, ranging from relocation rules to metaheuristics. Relocation rules (RRs) are used when the goal is to obtain a solution of acceptable quality in short time. Manually designing RRs is difficult and time-consuming, which motivates the use of different methods to automatically design RRs. In this study, we investigate the application of genetic programming (GP) to design RRs for CRP with multiple bays and container groups. The GP algorithm was adapted for generating RRs by proposing a new set of terminals and several solution construction methods. The proposed method was evaluated on an extensive benchmark of existing problems. The results obtained with automatically developed RRs were compared with the results of manually designed RRs and it was found that the automatically designed RRs performed significantly better in all cases.

Cartesian genetic programming for image analysis of the developing drosophila eye

Automatic feature extraction methods have gained increasing attention in modern image processing. The confocal images of the single-layered epithelium of the developing Drosophila eye may form an excellent model system to develop methods for complex feature extraction. The aim of this work was to explore Cartesian genetic programming for determination of the boundaries of ommatidia, the light-sensitive units in the presumptive eye region. Application of Cartesian genetic programming for the analysis of Fasciclin III expression has shown good results. This opens interesting perspectives for further use of this technology in the automatic analysis of confocal images.

Application of genetic programming in presenting novel equations for longitudinal dispersion coefficient in natural streams considering rivers geometry - Implementation in assimilation capacity simulation

Precise estimation of the longitudinal dispersion coefficient (LDC) is crucial for the accurate simulation of water quality management tools such as assimilation capacity. Previous research analyzed the LDC of natural streams in two general categories: ignoring or considering the river sinuosity (σ). Genetic programming (GP) is used in this study to investigate both mentioned categories by applying two experimental datasets from 56 to 24 different rivers worldwide. The first proposed LDC equation of this research (without σ) improves the amounts of statistical measures R2 (Determination Coefficient), OI (Overall Index), NSE (Nash-Sutcliffe Efficiency), WI (Willmott's Index of Agreement), RMSE (Root Mean Square Error), and MAE (Mean Absolute Error) by 3.75%, 4.71%, 7.81%, 0.85%, 13.72%, and 0.68%, respectively, compared to the best values of these indicators in the previous investigations. Regarding the second category, relative and absolute sensitivity analyses are conducted, which reveal that σ is the most influential parameter in the accurate prediction of the LDC among all hydraulics and geometric parameters of the river. This part of the investigation presents four unique LDC equations that closely match the experimental results. Significant improvement of the most accurate presented LDC for statistical indices R2, OI, NSE, WI, RMSE, MAE, and accuracy percentage are obtained equal to 3.27%, 2.41%, 3.16%, 0.81%, 35.1%, 24.47%, 3.8%, respectively, in comparison with the best previous relations. Also, a new indicator for measuring the efficiency of mathematical equations called Mean Normalized Statistical Index (MNSI) is introduced and applied in different parts of this research. Finally, the assimilation capacity of the Kashafrud River is determined based on the analytical method of pollution propagation for three types of water demands utilizing the accurately presented LDC in 1993–2020. The average amount of river assimilation capacity using accurate LDC is simulated at 91.93 tons/day, much lower than the currently reported pollution entrance, which equals 540 tons/day.

Customer Segmentation with RFM Model using Fuzzy C-Means and Genetic Programming

One of the strategies a company uses to retain its customers is Customer Relationship Management (CRM). CRM manages interactions and supports business strategies to build mutually beneficial relationships between companies and customers. The utilization of information technology, such as data mining used to manage the data, is critical in order to be able to find out patterns made by customers when processing transactions. Clustering techniques are possible in data mining to find out the patterns generated from customer transaction data. Fuzzy C-Means (FCM) is one of the best-known and most widely used fuzzy grouping methods. The iteration process is carried out to determine which data is in the right cluster based on the objective function. The local minimum is the condition where the resulting value is not the lowest value from the solution set. This research aims to solve the minimum local problem in the FCM algorithm using Genetic Programming (GP), which is one of the evolution-based algorithms to produce better data clusters. The result of the research is to compare the application of fuzzy c-means (FCM) and genetic programming fuzzy c-means (GP-FCM) for customer segmentation applied to the Cahaya Estetika clinic dataset. The test results of the GP-FCM yielded an objective function of 20.3091, while for the FCM algorithm, it was 32.44741. Furthermore, evaluating cluster validity using Partition Coefficient (PC), Classification Entropy (CE), and Silhouette Index proves that the results of cluster quality from gp-fcm are more optimal than fcm. The results of this study indicate that the application of genetic programming in the fuzzy c-means algorithm produces more optimal cluster quality than the fuzzy c-means algorithm.

MeterGPX: A Smart Multimeter Embedded with Multigene Genetic Programming Model for Multiarray Antenna Transmitter

Genetic programming (GP) is an evolutionary algorithm used to produce high-quality solutions to various problems. It has seen few claims in circuitry and the development of antenna designs. The application of GP in the model of embedded digital systems on multi-channel antenna arrays of subsurface imaging equipment has not yet been investigated. This study focuses on designing and developing a digital multimeter embedded with a multigene genetic programming (MGGP) model for multi-array transmitter antenna used for subsurface imaging operating at a low frequency of 3.5 kHz to 18.5 kHz using Arduino microcontroller for prototyping. The electrical outputs of a transmitter antenna system employed in a subsurface imaging device require live measurement and monitoring during operation for data logging purposes. The amount of transmitted voltage, produced current, and operating frequency are significant parameters for mapping the underground resistivity, thus the produced GP models are functions of the three parameters. GP fitness function was determined through MATLAB software. The output current signal from the transmitter were imitated in Proteus simulation software using a current source in the designed current measuring circuit. This produced linear and nonlinear relationships of the electrical outputs where GP modeling was beneficially applied. The application of GP in with the microcontroller provided an accurate reading of frequency, current and voltage produced by the multi-array transmitter antenna. These measurements were displayed using LM016L LCD display. Moreover, this embedded digital multimeter on transmitter antenna avoids utilizing costly high voltage measuring devices.

Binary text classification using genetic programming with crossover-based oversampling for imbalanced datasets

It is well known that classifiers trained using imbalanced datasets usually have a bias toward the majority class. In this context, classification models can present a high classification performance overall and for the majority class, even when the performance for the minority class is significantly lower. This paper presents a genetic programming (GP) model with a crossover-based oversampling technique for oversampling the imbalanced dataset for binary text classification. The aim of this study is to apply an oversampling technique to solve the imbalanced issue and improve the performance of the GP model that employed the proposed technique. The proposed technique employs a crossover operator for generating new samples for the minority class in an imbalanced text dataset. By using a combination of this crossover-based oversampling technique with GP, the performance was improved. It is shown that the proposed combination outperforms all GP applications that use the original dataset without resampling. Moreover, the performance of the proposed system surpassed GP approaches using the synthetic minority oversampling technique (SMOTE) and random oversampling. Further comparison with the state-of-the-art on five imbalanced text datasets in terms of F1-score shows the superior performance of the proposed approach.

MATHEMATICAL MODELLING OF THE CO2 LASER CUTTING PROCESS USING GENETIC PROGRAMMING

The development of mathematical models by using experimental data is of great importance for modelling and optimization of the laser cutting process. Motivated by the lack of research regarding the use of genetic programming (GP) for deriving empirical mathematical models that describe the laser cutting process, the present study discusses the application of GP to the development of a kerf taper angle mathematical model. The aim was to quantify the relationship between three selected input parameters (cutting speed, laser power and assist gas pressure) and kerf taper angle using GP in the CO2 laser cutting of aluminium alloy AlMg3. To obtain the experimental database for the GP model evolution process, a laser cutting experiment was planned as per standard full factorial design where all three selected parameters were varied at three levels. The fit between the experimental and the GP model prediction values of kerf taper angle was found to be appropriate. Finally, by using the derived GP mathematical model, the analysis of the effects of input parameters on the change in kerf taper angle values was performed by generating 3D surface plots.

Machine learning-based downscaling: application of multi-gene genetic programming for downscaling daily temperature at Dogonbadan, Iran, under CMIP6 scenarios

Machine learning-based downscaling: application of multi-gene genetic programming for downscaling daily temperature at Dogonbadan, Iran, under CMIP6 scenarios

Experimental evaluation and application of genetic programming to develop predictive correlations for hydrochar higher heating value and yield to optimize the energy content

The hydrothermal carbonization (HTC) process has been found to consistently improve biomass fuel characteristics by raising the higher heating value (HHV) of the hydrochar as process severity is increased. However, this is usually associated with a decrease in the solid yield (SY) of hydrochar, making it difficult to determine the optimal operating conditions to obtain the highest energy yield (EY), which combines the two parameters. In this study, a graph-based genetic programming (GP) method was used for developing correlations to predict HHV, SY, and EY for hydrochars based on published values from 42 biomasses and a broad range of HTC experimental systems and operating conditions, i.e., 5 ≤ holding time (min) ≤ 2208, 120 ≤ temperature (°C) ≤ 300, and 0. 0096 ≤ biomass to water ratio ≤ 0.5. In addition, experiments were carried out with 5 pomaces at 4 temperatures and two reactor scales, 1 L and 18.75 L. The correlations were evaluated using this experimental data set in order to estimate prediction errors in similar experimental systems. The use of the correlations to predict HTC conditions to achieve the maximum EY is demonstrated for three common feedstocks, wheat straw, sewage sludge, and a fruit pomace. The prediction was confirmed experimentally with pomace at the optimized HTC conditions; we observed 6.9 % error between the measured and predicted EY %. The results show that the correlations can be used to predict the optimal operating conditions to produce hydrochar with the desired fuel characteristics with a minimum of actual HTC runs.

Automated design of heuristics for the container relocation problem using genetic programming

The container relocation problem is a challenging combinatorial optimisation problem tasked with finding a sequence of container relocations required to retrieve all containers by a given order. Due to the complexity of this problem, heuristic methods are often applied to obtain acceptable solutions in a small amount of time. These include relocation rules (RRs) that determine the relocation moves that need to be performed to efficiently retrieve the next container based on certain yard properties. Such rules are often designed manually by domain experts, which is a time-consuming and challenging task. This paper investigates the application of genetic programming (GP) to design effective RRs automatically. Experimental results show that RRs evolved by GP outperform several existing manually designed RRs. Additional analyses of the proposed approach demonstrate that the evolved rules generalise well across a wide range of unseen problems and that their performance can be further enhanced. Therefore, the proposed method presents a viable alternative to existing manually designed RRs and opens a new research direction in the area of container relocation problems.

Application of dimensional analysis and multi-gene genetic programming to predict the performance of tunnel boring machines

An accurate prediction of tunnel boring machine (TBM) performance is one of the complex and crucial issues encountered frequently in tunnel construction, which is the aim of the present study. An improved methodology using dimensional analysis (DA) and multi-gene genetic programming (MGGP) is proposed to obtain a practical and accurate model which can predict TBM performance. Three dimensionless parameters are introduced by applying DA to predict TBM performance more efficiently. These parameters can represent TBM and rock features. The MGGP, as a powerful technique for developing a practical correlation model, was adopted to develop highly accurate models using GPTIPS (Genetic Programming Toolbox for the Identification of Physical Systems). A well-known database of a hard rock mechanized tunneling project of the Queens water conveyance tunnel was used to evaluate the performance of the proposed methodology. The performances of the developed models were examined and compared with other reported models using three statistical criteria. Regarding the sum of squared deviations (SSD), the developed model yielded 21.7% better results than the best existing model. Moreover, it was found that the presented dimensionless parameters have physical meaning and are much better parameters to develop a model for TBM performance prediction.

Exploring the practical application of genetic programming for stormwater drain inlet hydraulic efficiency estimation

Exploring the practical application of genetic programming for stormwater drain inlet hydraulic efficiency estimation

Prediction of the Height of Fracturing via Gene Expression Programming in Australian Longwall Panels: A Comparative Study

The caving and subsidence developments above a longwall panel usually result in fractures of the overburden, which decrease the strength of the rock mass and its function. The height of fracturing (HoF) includes the caved and continuous fractured zones affected by a high degree of bending. Among the various empirical models, Ditton’s geometry and geology models are widely used in Australian coalfields. The application of genetic programming (GP) and gene expression programming (GEP) in longwall mining is entirely new and original. This work uses a GEP method in order to predict HoF. The model variables, including the panel width (W), cover depth (H), mining height (T), unit thickness (t), and its distance from the extracted seam (y), are selected via the dimensional analysis and Buckingham’s P-theorem. A dataset involving 31 longwall panels is used to present a new nonlinear regression function. The statistical estimators, including the coefficient of determination (R2), the average error (AE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE), are used to compare the performance of the discussed models. The R2 value for the GEP model (99%) is considerably higher than the corresponding values of Ditton’s geometry (61%) and geology (81%) models. Moreover, the maximum values of the statistical error estimators (AE, MAPE, and RMSE) for the GEP model are 12%, 14%, and 16%, respectively, of the corresponding values of Ditton’s models.

Multi-objective design optimization of battery thermal management system for electric vehicles

• Genetic programming shows reliable surrogate modelling. • Properties of liquid-based battery thermal management system are analyzed. • Cooling plate design and inlet coolant velocity are the main factors affect the temperature uniformity and pressure drop. • The pressure drop has been significantly optimized with the proposed optimization framework. Lithium batteries are commonly used as the primary power storage unit for electric vehicles, and their performance is sensitive to temperature. Thus, the battery thermal management system is crucially needed to allow the EVs to work safely and efficiently. This paper mainly focuses on the performance analysis and design optimization of the battery thermal management system with a U-shaped cooling channel. A Computational fluid dynamics model of a battery thermal management system is built to study the battery temperature distribution and pressure distribution. Through the establishment of the genetic programming model, sensitivity analysis and parameter interaction analysis are carried out to analyze the influence of cooling plate thickness, cooling plate wall thickness, inlet coolant temperature and flow velocity on the comprehensive performance of the battery thermal management system. A new surrogate-assisted multi-objective optimization scheme is proposed by introducing an integrated AI system that includes a surrogate battery model built with genetic programming (GP) and a design optimizer driven by the second-generation non-dominated sorting genetic algorithm (NSGA-II). Results show that the inlet coolant temperature has the most significant influence on the rise of battery temperature (59.87%) but has no influence on the pressure drop. The structural parameters of the cooling plate and the velocity of the inlet coolant have apparent effects on the uniformity of the battery temperature distribution and the pressure drop. The battery thermal management system achieves an ideal comprehensive performance when the thickness of the cooling plate is 4.50 mm, the thickness of the cooling plate wall is 1.49 mm, the inlet coolant temperature is 298.15 K, and the inlet coolant velocity is 0.29 m/s. Under such optimized parameter settings, the max temperature rise of the battery reduces from 7.72 K to 7.69 K, the standard deviation of the temperature distribution 2.54 K (a drop of 0.02 K), and the pressure drop decrease from 1022.1 Pa to 436.43 Pa (decrease by 57.3%). Such results have guiding significance for the design of the battery thermal management system with a U-shaped channel and the application of genetic programming in system performance analysis and optimization.