Use Of Genetic Programming Research Articles

Genetic Programming for Automatically Evolving Multiple Features to Classification

Abstract Performing classification on high-dimensional data poses a significant challenge due to the huge search space. Moreover, complex feature interactions introduce an additional obstacle. The problems can be addressed by using feature selection to select relevant features or feature construction to construct a small set of high-level features. However, performing feature selection or feature construction only might make the feature set suboptimal. To remedy this problem, this study investigates the use of genetic programming for simultaneous feature selection and feature construction in addressing different classification tasks. The proposed approach is tested on 16 datasets and compared with seven methods including both feature selection and feature constructions techniques. The results show that the obtained feature sets with the constructed and/or selected features can significantly increase the classification accuracy and reduce the dimensionality of the datasets. Further analysis reveals the complementarity of the obtained features leading to the promising classification performance of the proposed method.

A dynamic trust management model for vehicular ad hoc networks

Trust management in vehicular ad hoc networks (VANETs) is a challenging dynamic optimization problem due to their decentralized, infrastructureless, and dynamically changing topology. Evolutionary computation (EC) algorithms are good candidates for solving dynamic optimization problems (DOPs), since they are inspired from the biological evolution that is occurred as a result of changes in the environment. In this study, we explore the use of genetic programming (GP) algorithm and evolutionary dynamic optimization (EDO) techniques to build a dynamic trust management model for VANETs. The proposed dynamic trust management model properly evaluates the trustworthiness of vehicles and their messages in the simulation of experimental scenarios including bogus information attacks. The simulation results show that the evolved trust calculation formula prevents the propagation of bogus messages over VANETs successfully and the dynamic trust management model detects changes in the problem and reacts to them in a timely manner. The best evolved formula achieves 89.38% Matthews Correlation Coefficient (MCC), 91.81% detection rate (DR), and 1.01% false positive rate (FPR), when ≈ 5% of the network traffic is malicious. The formula obtains 87.33% MCC, 92.01% DR, and 4.8% FPR when ≈ 40% of the network traffic is malicious, demonstrating its robustness to increasing malicious messages. The proposed model is also run on a real-world traffic model and obtains high MCC and low FPR values. To the best of our knowledge, this is the first application of EC and EDO techniques that generate a trust formula automatically for dynamic trust management in VANETs.

Using genetic programming and the stress equilibrium method to obtain the un-stressed lattice parameter for calculating residual stresses

While it is known that the crucial requirement for investigating residual stresses using diffraction is the use of a reliable unstressed lattice parameter, the robustness of genetic programming to accomplish this task will be shown here. The parameter obtained from genetic programming in the context of the stress equilibrium method is compared with values resulting from other approaches of this method. This gives support and strength to the use of genetic programming to investigate microscopic residual stresses from real, experimental information. This is, so far, absent in theoretical models recently proposed.Whereas residual stress fields determination by diffraction methods at a macroscopic scale (scale of the sample size) offers no serious difficulties, the stress determination at the microscopic scale (i.e., stresses varying among neighboring grains) is still a pending task. Understanding these microscopic stresses is, however, of a great technological importance, as they may be the cause of fatigue damage and/or stress corrosion cracking in many structural components. Despite that theoretical but solid alternatives, for example those based on phase field models, are being used to unveil the stresses developed at the grain scale after known thermo-mechanical treatments, the results obtained still need to be assessed by experimental results linked to real microstructures. On the contrary, recent works propose the use of genetic programming approaches to investigate these microscopic stresses on the basis of data recorded from real stressed samples; specifically, from neutron diffraction and detailed knowledge of the microstructure and its characteristics; e.g., the texture gradient developed.

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.

Modeling the optical properties of transparent and absorbing dielectrics by means of symbolic regression.

In this contribution we explore the possibilities and limitations of symbolic regression as an alternative to the approaches currently used to characterize the dispersive behavior of a given material. To this end, we make use of genetic programming to retrieve, from either ellipsometric or spectral data, closed-form expressions that model the optical properties of the materials studied. In a first stage we consider transparent dielectrics for our numerical experiments. Next we increase the complexity of the problem and consider absorbing dielectrics, which not only require the use of complex functions to model their dielectric function, but also imply a supplementary constraint imposed by the verification of the causality principle.

Education Teaching Evaluation Method Aided by Adaptive Genetic Programming and Robust Scheduling

Process mining technology aims to automatically generate process models by analyzing events, thereby assisting the design and redesign of process models. Although many process mining methods have appeared, they all have deficiencies. These methods focus on mining from the behavioral aspects described by the log, while ignoring the structural nature of the process model itself. The complexity of the process describes the simplicity and ease of understanding of the process. Higher process complexity affects the readability of the process. Genetic programming has strong robustness. Its individual representation based on tree structure can describe the special structure of the process. The introduction of process complexity fitness enables it to consider the complexity of the process model itself while mining log behavior, so as to achieve nonlinear mining of complex processes. This paper analyzes the process mining based on genetic programming and proposes process individuals based on tree structure. By realizing the combination of process complexity measurement and process mining technology, noncomplex process mining can be realized. In this paper, the process complexity is combined with the process mining algorithm based on genetic programming, and a measure of process structural complexity is proposed, which is converted into complexity fitness and introduced into the fitness function of genetic programming, to realize the use of genetic programming. The research results show that the improved new adaptive genetic programming robust scheduling algorithm provides a new idea and method for the evaluation of college sports under different sports conditions. This makes the college sports evaluation management system more intelligent and improves the rational allocation of physical education teaching.

From Requirements to Source Code: Evolution of Behavioral Programs

Automatically generating executable code has a long history of arguably modest success, mostly limited to the generation of small programs of up to 200 lines of code, and genetic improvement of existing code. We present the use of genetic programming (GP) in conjunction with context-oriented behavioral programming (COBP), the latter being a programming paradigm with unique characteristics that facilitate automatic coding. COBP models a program as a set of behavioral threads (b-threads), each aligned to a single behavior or requirement of the system. To evolve behavioral programs we design viable and effective genetic operators, a genetic representation, and evaluation methods. The simplicity of the COBP paradigm, its straightforward syntax, the ability to use verification and formal-method techniques to validate program correctness, and a program comprising small independent chunks all allow us to effectively generate behavioral programs using GP. To demonstrate our approach we evolve complete programs from scratch of a highly competent O player for the game of tic-tac-toe. The evolved programs are well structured, consisting of multiple, explainable modules that specify the different behavioral aspects of the program and are similar to our handcrafted program. To validate the correctness of our individuals, we utilize the mathematical characteristics of COBP to analyze program behavior under all possible execution paths. Our analysis of an evolved program proved that it plays as expected more than 99% of the times.

Towards a Vectorial Approach to Predict Beef Farm Performance

Accurate livestock management can be achieved by means of predictive models. Critical factors affecting the welfare of intensive beef cattle husbandry systems can be difficult to be detected, and Machine Learning appears as a promising approach to investigate the hundreds of variables and temporal patterns lying in the data. In this article, we explore the use of Genetic Programming (GP) to build a predictive model for the performance of Piemontese beef cattle farms. In particular, we investigate the use of vectorial GP, a recently developed variant of GP, that is particularly suitable to manage data in a vectorial form. The experiments conducted on the data from 2014 to 2018 confirm that vectorial GP can outperform not only the standard version of GP but also a number of state-of-the-art Machine Learning methods, such as k-Nearest Neighbors, Generalized Linear Models, feed-forward Neural Networks, and long- and short-term memory Recurrent Neural Networks, both in terms of accuracy and generalizability. Moreover, the intrinsic ability of GP in performing an automatic feature selection, while generating interpretable predictive models, allows highlighting the main elements influencing the breeding performance.

Genetic programming-based hyper-heuristic approach for solving dynamic job shop scheduling problem with extended technical precedence constraints

Extended technical precedence constraints (ETPC) in dynamic job shop scheduling problem (DJSP) are the precedence constraints existing between different jobs instead of the conventional technical precedence constraints existing in the operations of the same job. This paper presents the mathematical programming model of the DJSP with ETPC to minimize the mean weighted tardiness of the jobs. The mathematical model contributes to the solution and modelling of the DJSP with ETPC and it is used to solve small-sized problems to optimality. To solve industry-sized problems, a constructive heuristic called the dispatching rule (DR) is employed. This paper investigates the use of genetic programming (GP) as a hyper-heuristic in the automated generation of the problem-specific DRs for solving the problem under consideration. The genetic programming-based hyper heuristic (GPHH) approach constructs the DRs which are learned from the training instances and then verified on the test instances by the simulation experiments. To enhance the efficiency of the approach when evolving effective DRs to solve the problem, the approach is improved with strategies which consist of a problem-specific attribute selection for GP and a threshold condition mechanism for fitness evaluation. The simulation results verify the effectiveness and efficiency of the evolved DRs to the problem under consideration by comparing against the existing classical DRs. The statistical analysis of the simulation results shows that the evolved DRs outperform the selected benchmark DRs on the problem under study. The sensitivity analysis also shows that the DRs generated by the GPHH approach are robust under different scheduling performance measures. Moreover, the effects of the model parameters, including the percentage of jobs with ETPC and the machine utilization, on the performance of the DRs are investigated.

Sentiment analysis with genetic programming

With the advent of online social networks, people became more eager to express and share their opinions and sentiment about all kinds of targets. The overwhelming amount of opinion texts soon attracted the interest of many entities (industry, e-commerce, celebrities, etc.) that were interested in analyzing the sentiment people express about what they produce or communicate. This interest has led to the surge of the sentiment analysis (SA) field. One of the most studied subfields of SA is polarity detection, which is the problem of classifying a text as positive, negative, or neutral. This classification problem is difficult to solve automatically, and many hand-adjusted resources are needed to overcome the difficulties in detecting sentiment from text. These resources include hand-adjusted textual features as well as lexicons. Deciding which resource and which combination of resources are more appropriate to a given scenario is a time-consuming trial-and-error process. Thus, in this work, we propose the use of Genetic Programming (GP) as a tool for automatically choosing, combining, and classifying sentiment from text. We propose a series of functions that allow GP to deal with preprocessing tasks, handcrafted features, and automatic weighting of lexicons for a given training set. Our experiments show that our GP solution is competitive and sometimes better than SVM and superior to naïve Bayes, logistic regression, and stochastic gradient descent, which are methods used in SA competitions.

Genetic programming approach and data generation for transfer lengths in pretensioned concrete members

This study aims to derive a practical equation that can predict the transfer length of prestressing strands with the use of genetic programming. Towards this end, a total of 260 transfer length test results were collected from previous studies, and a feature selection procedure was applied to the collected database to extract the key features influencing the transfer length. Based on the five most important features, a practical equation was derived using a genetic programming approach, and the rationality of the proposed equation was verified by comparing it with design codes, existing models, and machine learning models (random forest and artificial neural network). In addition, 1.0 × 104 fake transfer length data that follow the probability distribution of the real data were generated using a generative adversarial network, based on which the prediction performances were visualized and compared in detail. The results showed that the proposed equation exhibited a higher level of accuracy than other existing equations.

Polymer viscosifier systems with potential application for enhanced oil recovery: a review

Due to the growing demand for oil and the large number of mature oil fields, Enhanced Oil Recovery (EOR) techniques are increasingly used to increase the oil recovery factor. Among the chemical methods, the use of polymers stands out to increase the viscosity of the injection fluid and harmonize the advance of this fluid in the reservoir to provide greater sweep efficiency. Synthetic polymers based on acrylamide are widely used for EOR, with Partially Hydrolyzed Polyacrylamide (PHPA) being used the most. However, this polymer has low stability under harsh reservoir conditions (High Temperature and Salinity – HTHS). In order to improve the sweep efficiency of polymeric fluids under these conditions, Hydrophobically Modified Associative Polymers (HMAPs) and Thermo-Viscosifying Polymers (TVPs) are being developed. HMAPs contain small amounts of hydrophobic groups in their water-soluble polymeric chains, and above the Critical Association Concentration (CAC), form hydrophobic microdomains that increase the viscosity of the polymer solution. TVPs contain blocks or thermosensitive grafts that self-assemble and form microdomains, substantially increasing the solution’s viscosity. The performance of these systems is strongly influenced by the chemical group inserted in their structures, polymer concentration, salinity and temperature, among other factors. Furthermore, the application of nanoparticles is being investigated to improve the performance of injection polymers applied in EOR. In general, these systems have excellent thermal stability and salinity tolerance along with high viscosity, and therefore increase the oil recovery factor. Thus, these systems can be considered promising agents for enhanced oil recovery applications under harsh conditions, such as high salinity and temperature. Moreover, stands out the use of genetic programming and artificial intelligence to estimate important parameters for reservoir engineering, process improvement, and optimize polymer flooding in enhanced oil recovery.

A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis

This paper proposes a machine learning approach dealing with genetic programming to build classifiers through logical rule induction. In this context, we define and test a set of mutation operators across from different clinical datasets to improve the performance of the proposal for each dataset. The use of genetic programming for rule induction has generated interesting results in machine learning problems. Hence, genetic programming represents a flexible and powerful evolutionary technique for automatic generation of classifiers. Since logical rules disclose knowledge from the analyzed data, we use such knowledge to interpret the results and filter the most important features from clinical data as a process of knowledge discovery. The ultimate goal of this proposal is to provide the experts in the data domain with prior knowledge (as a guide) about the structure of the data and the rules found for each class, especially to track dichotomies and inequality. The results reached by our proposal on the involved datasets have been very promising when used in classification tasks and compared with other methods.

Towards the use of genetic programming in the ecological modelling of mosquito population dynamics

Predictive algorithms are powerful tools to support infection surveillance plans based on the monitoring of vector abundance. In this article, we explore the use of genetic programming (GP) to build a predictive model of mosquito abundance based on environmental and climatic variables. We claim, in fact, that the heterogeneity and complexity of this kind of dataset demands algorithms capable of discovering complex relationships among variables. For this reason, we benchmarked GP performance with state of the art machine learning predictive algorithms. In order to provide a real exploitable model of mosquito abundance, we trained GP and the other algorithms on mosquito collections from 2002 to 2005 and we tested the predictive ability in 2006 collections. Results reveal that, among the studied methods, GP has the best performance in terms of accuracy and generalization ability. Moreover, the intrinsic feature selection and readability of the solution provided by GP offer the possibility of a biological interpretation of the model which highlights known or new behaviours responsible for mosquito abundance. GP, therefore, reveals to be a promising tool in the field of ecological modelling, opening the way to the use of a vector based GP approach (VE-GP) which may be more appropriate and beneficial for the problems in analysis.

Symbolic regression of uncertainty-resilient inferential sensors for fault diagnostics

An algorithm is presented for the design of inferential sensors for fault diagnostics in thermal management systems. The algorithm uses input and output sensed system information to improve the detection and isolation of a fault by generating inferential sensors that augment the measured information to: (i) reduce the evidence of uncertainty in the inferred variables, and thus decrease false alarm and nondetection rates; and (ii) provide distinguishable responses to faults, and thus reduce reduce the rate of misdiagnoses. The novelty of the algorithm is its use of genetic programming to evolve explainable inferential sensors that maximize information criteria specific to fault diagnostics. The chosen criteria: (i) least squares regression; and (ii) Ds -optimality (calculated from the Fisher Information Matrix), leverage symbolic mathematics and automatic differentiation to obtain parametric sensitivities of the measured outputs and inferential sensors. The algorithm is included in a standard work for fault diagnostics, where its effectiveness is assessed through k-NN classification and illustrated in an application to an aircraft cross-flow plate-fin heat exchanger.

Knowledge elicitation based on genetic programming for non destructive testing of critical aerospace systems

In non-destructive testing of aerospace structures’ defects, the tests reliability is a crucial issue for guaranteeing security of both aircrafts and passengers. Most of the widely recognized approaches rely on precision and reliability of testing equipment, but also the methods and techniques used for processing measurement results, in order to detect defects, may heavily influence the overall quality of the testing process. The effectiveness of such methods strongly depends on specific field knowledge that is definitely not easy to be formalized and codified within the results processing practices. Although many studies have been conducted in this direction, such issue is yet an open-problem. This work describes the use of Genetic Programming for the diagnosis and modeling of aerospace structural defects. The resulting approach aims at extracting such knowledge by providing a mathematical model of the considered defects, which can be used for recognizing other similar ones. Eddy-Current Testing has been selected as a case study in order to assess both the performance and functionality of the whole framework, and a publicly available dataset of specific measures for aircraft structures has been considered. The experimental results put into evidence the effectiveness of the proposed approach in building reliable models of the aforementioned defects, so that it can be considered a successful option for building the knowledge needed by tools for controlling the quality of critical aerospace systems.

The impact of genetic programming in education

Since its inception genetic programming, and later variations such as grammar-based genetic programming and grammatical evolution, have contributed to various domains such as classification, image processing, search-based software engineering, amongst others. This paper examines the role that genetic programming has played in education. The paper firstly provides an overview of the impact that genetic programming has had in teaching and learning. The use of genetic programming in intelligent tutoring systems, predicting student performance and designing learning environments is examined. A critical analysis of genetic programming in education is provided. The paper then examines future directions of research and challenges in the application of genetic programming in education.

Genetic Programming: A Complementary Approach for Discharge Modelling in Smooth and Rough Compound Channels

Use of genetic programming (GP) in the field of river engineering is rare. During flood when the water overflows beyond its main course known as floodplain encounters various obstacles through rough materials and vegetation. Again the flow behaviour becomes more complex in a compound channel section due to shear at different regions. Discharge results from the experimental channels for varying roughness surfaces, along with data from a compound river section, are used in the GP. Model equations are derived for prediction of discharge in the compound channel using five hydraulic parameters. Derived models are tested and compared with other soft computing techniques. Few performance parameters are used to evaluate all the approaches taken for analysis. From the sensitivity analysis, the effects of parameters responsible for the flow behaviour are inferred. GP is found to give the most potential results with the highest level of accuracy. This work aims to benefit the researchers studying machine learning approaches for application in stream flow analysis.

Observing and Forecasting the Trajectory of the Thrown Body with use of Genetic Programming

Observing and Forecasting the Trajectory of the Thrown Body with use of Genetic Programming

APPLICATION OF GENETIC PROGRAMMING TOOLS AS A MEANS OF SOLVING OPTIMIZATION PROBLEMS

Purpose. The article is devoted to the problem of practical application of genetic programming tools as a means of solving optimization problems and the use of genetic programming in various fields of activity. It is established that the evolution of genetic programming is directly related to the development of the genetic algorithm, it is also determined that with the passage of time a significant improvement in genetic programming has occurred. Since the advent of the genetic algorithm, many modifications and software implementations have appeared. This in turn led to the implementation of the genetic algorithm toolkit in software products, namely: specialized software, applications for mathematical and analytical packages, frameworks and libraries. The article reveals the significant impact of genetic programming in the areas of: quantum computing, electrical circuit design, etc. Not only advantages, but also disadvantages are considered, attention is also paid to methods of eliminating deficiencies by improving optimization methods and applying a genetic algorithm. Results. The analysis of the main directions of the practical use of genetic programming is carried out and tasks that can be effectively solved using this toolkit are outlined. Scientific novelty. It was determined that the improvement of optimization methods and the expansion of the use of genetic algorithms, stimulates the appearance of such software products on the market, simplifies the structure of software tools, designs the interface for working with a specific commercial user community, simplifies the command language, which allows the use of genetic programming tools circle of users with different levels of training.