Application Of Genetic Programming Research Articles

Application of Artificial Neural Network and Genetic Programming in Modeling and Optimization of Ultraviolet Water Disinfection Reactors

Ultraviolet (UV) disinfection is an environmental-friendly technology for water treatment. However, design and operation of UV disinfection reactors are very difficult without a good model. In this work, two modeling methods, Artificial Neural Network (ANN) and Genetic Programming (GP), were applied to model UV water disinfection reactors. The model training data were obtained from simulation using Computational Fluid Dynamics (CFD) software. The accuracy of these two modeling methods was compared based on modeling error as well as generalization ability for new inputs. The ANN and GP models were then used to determine optimal design and operating variables of UV disinfection process, using multi-objective optimization. Selected Pareto-optimal solutions were compared using CFD simulations, and the results are presented and discussed in this paper.

Application of genetic programming and model checking for finite-state machine induction

In this paper, we describe the method of finite state machine induction using genetic programming with fitness based on testing and model checking. The description of chromosome structure, of genetic algorithm, of cross-over and mutation operations is given. Experimental results for the induction of elevator doors control system are given.

Genetic Programming and Serial Processing for Time Series Classification

This work describes an approach devised by the authors for time series classification. In our approach genetic programming is used in combination with a serial processing of data, where the last output is the result of the classification. The use of genetic programming for classification, although still a field where more research in needed, is not new. However, the application of genetic programming to classification tasks is normally done by considering the input data as a feature vector. That is, to the best of our knowledge, there are not examples in the genetic programming literature of approaches where the time series data are processed serially and the last output is considered as the classification result. The serial processing approach presented here fills a gap in the existing literature. This approach was tested in three different problems. Two of them are real world problems whose data were gathered for online or conference competitions. As there are published results of these two problems this gives us the chance to compare the performance of our approach against top performing methods. The serial processing of data in combination with genetic programming obtained competitive results in both competitions, showing its potential for solving time series classification problems. The main advantage of our serial processing approach is that it can easily handle very large datasets.

A genetic programming approach to river flow modeling

This paper proposes the application of genetic programming (GP) to forecast monthly river flow. The river flow models were improved by the monthly rainfall and flow data from three stations for Kizilirmak River, Turkey. The coefficient of determination (R2) and root mean square error (RMSE) values were used for evaluating the accuracy of the developed models. The most appropriate GP model was determined as model having monthly flow data of Yamula and Bulakbasi stations according to the model performance criteria for testing data set. The models obtained using the GP were compared with multiple linear regression (MLR) techniques in river flow forecasting. The comparison results revealed that the suggested GP model performs quite well compared to MLR models. It was shown that the suggested GP model with R2 = 0.96 and RMSE = 8.02 m3/s for testing period could be used in planning and management of water resources.

Comparing Predictability of Genetic Programming and ANFIS on Drilling Performance Modeling for GFRP Composites

Drilling of glass fiber reinforced polymer (GFRP) composite material is substantially complicated from the metallic materials due to its high structural stiffness (of the composite) and low thermal conductivity of plastics. During drilling of GFRP composites, problems generally arise like fiber pull out, delamination, stress concentration, swelling, burr, splintering and micro cracking etc. which reduces overall machining performance. Now-a-days hybrid approaches have been received remarkable attention in order to model machining process behavior and to optimize machining performance towards subsequent improvement of both quality and productivity, simultaneously. In the present research, spindle speed, feed rate, plate thickness and drill bit diameter have been considered as input parameters; and the machining yield characteristics have been considered in terms of thrust and surface roughness (output responses) of the drilled composite product. The study illustrates the applicability of genetic programming with the help of GPTIPS as well as Adaptive Neuro Fuzzy Inference System (ANFIS) towards generating prediction models for better understanding of the process behavior and for improving process performances in drilling of GFRP composites.

Estimation of mechanical properties of nanomaterials using artificial intelligence methods

Computational modeling tools such as molecular dynamics (MD), ab initio, finite element modeling or continuum mechanics models have been extensively applied to study the properties of carbon nanotubes (CNTs) based on given input variables such as temperature, geometry and defects. Artificial intelligence techniques can be used to further complement the application of numerical methods in characterizing the properties of CNTs. In this paper, we have introduced the application of multi-gene genetic programming (MGGP) and support vector regression to formulate the mathematical relationship between the compressive strength of CNTs and input variables such as temperature and diameter. The predictions of compressive strength of CNTs made by these models are compared to those generated using MD simulations. The results indicate that MGGP method can be deployed as a powerful method for predicting the compressive strength of the carbon nanotubes.

Effect of specimen size and shape on compressive strength of concrete containing fly ash: Application of genetic programming for design

The use of fly ash as a mineral admixture in the manufacture of concrete has received considerable attention in recent years. For this reason, several experimental studies are carried out by using fly ash at different proportions replacement of cement in concrete. In the present study, the models are developed in genetic programming for predicting the compressive strength values of cube (100 and 150mm) and cylinder (100×200 and 150×300mm) concrete containing fly ash at different proportions. The experimental data of different mixtures are obtained by searching 36 different literatures to predict these models. In the set of the models, the age of specimen, cement, water, sand, aggregate, superplasticizers, fly ash and CaO are entered as input parameters, while the compressive strength values of concrete containing fly ash are used as output parameter. The training, testing and validation set results of the explicit formulations obtained by the genetic programming models show that artificial intelligent methods have strong potential and can be applied for the prediction of the compressive strength of concrete containing fly ash with different specimen size and shape.

Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks

Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks

Application of numerical modeling and genetic programming to estimate rock mass modulus of deformation

Estimation of the rock mass modulus of deformation (Em) is one of the most important design parameters in designing many structures in and on rock. This parameter can be obtained by in situ tests, empirical relations between deformation modulus and rock mass classification, and estimating from laboratory tests results. In this paper, a back analysis calculation is performed to present an equation for estimation of the rock mass modulus of deformation using genetic programming (GP) and numerical modeling. A database of 40,960 datasets, including vertical stress (σz), horizontal to vertical stresses ratio (k), Poisson’s ratio (ν), radius of circular tunnel (r) and wall displacement of circular tunnel on the horizontal diameter (d) for input parameters and modulus of deformation for output, was established. The selected parameters are easy to determine and rock mass modulus of deformation can be obtained from instrumentation data of any size circular galleries. The resulting RMSE of 0.86 and correlation coefficient of 97% of the proposed equation demonstrated the capability of the computer program (CP) generated by GP.

Application of Genetic Programming in Stage Hydrograph Routing of Open Channels

Routing is a technique used to simulate and predict changes in water flow along a river or channel. There are several hydraulic flow routing methods that model channel flow with high accuracy using lots of data related to channel geometry and specifications, thus making calculations very expensive. In contrast, hydrologic methods are techniques that simplify the calculation of flow conditions in a channel reach. In this paper, a stage hydrograph is modeled in simple and compound channels by genetic programming (GP) as a hydrologic method that does not depend on channel geometry and specifications, channel shape, and modeling time step. Routed hydrographs for simple and compound channels are then compared with a river analysis system model (HEC-RAS) and a coupled characteristic-dissipative-Galerkin procedure in one-dimension (CCDG-1D) as the hydraulic methods, respectively. Results show that the sum of squared differences (SSD) between a stage hydrograph by GP and modeled hydrographs by HEC-RAS and CCDG-1D methods, respectively, are not considerable in simple and compound channels. Moreover, GP is a capable tool to route an acceptable stage hydrograph even by using less geometry specification and time intervals in the detected stage. Those results indicate that the proposed GP method is effective in routing a stage hydrograph.

Towards new directions of data mining by evolutionary fuzzy rules and symbolic regression

There are various techniques for data mining and data analysis. Among them, hybrid approaches combining two or more fundamental methods gain importance as the complexity and dimension of real world problems and data sets grows. Fuzzy sets and fuzzy logic can be used for efficient data classification by the means of fuzzy rules and classifiers. This study presents an application of genetic programming to the evolution of fuzzy rules based on the concept of extended Boolean queries. Fuzzy rules are used as symbolic classifiers learned from data and used to label data records and to predict the value of an output variable. An example of the application of such a hybrid evolutionary-fuzzy data mining approach to a real world problem is presented.

Rutting prediction of asphalt mixtures modified by polypropylene fibers via repeated creep testing by utilising genetic programming

A novel application of genetic programming (GP) for modelling and presenting closed form solutions to the rutting prediction for polypropylene (PP) modified asphalt mixtures is investigated. Various PP fibers have been utilised for bitumen modification and repeated creep (RC) tests have been carried out. Marshall specimens, fabricated with multifilament 3 mm (M-03) type PP fibers at optimum bitumen content of 5% have been tested under different load values and patterns at 50 °C to investigate their rutting potential. It has been shown that the service lives of PP fiber-reinforced Marshall specimens are respectively longer than the control specimens under the same testing conditions (5 to 12 times). Input variables in the developed GP model use the physical properties of Marshall specimens such as PP type, specimen height, unit weight, voids in mineral aggregate, voids filled with asphalt, air voids, rest period and pulse counts. The performance of the accuracy of the proposed GP model is observed to be quite satisfactory. To obtain the main effects plot, detailed parametric studies have been performed. The presented closed form solution will also help further researchers willing to perform studies on the prediction of the rutting potential of asphalt without carrying out destructive tests for similar type of aggregate sources, bitumen, aggregate gradation, modification technique and laboratory conditions.

Automatic Machine Code Generation for a Transport Triggered Architecture using Cartesian Genetic Programming

Transport triggered architectures are used for implementing bio-inspired systems due to their simplicity, modularity and fault-tolerance. However, producing efficient, optimised machine code for such architectures is extremely difficult, since computational complexity has moved from the hardware-level to the software-level. Presented is the application of Cartesian Genetic Programming (CGP) to the evolution of machine code for a simple implementation of transport triggered architecture. The effectiveness of the algorithm is demonstrated by evolving machine code for a 4-bit multiplier with three different levels of parallelism. The results show that 100% successful solutions were found by CGP and by further optimising the size of the solutions, it’s possible to find efficient implementations of the 4-bit multiplier. Further analysis of the solutions showed that use of loops within the CGP function set could be beneficial and was demonstrated by repeating the earlier 4-bit multiplier experiment with the addition of a loop function.

Customizable execution environments for evolutionary computation using BOINC + virtualization

Evolutionary algorithms (EAs) consume large amounts of computational resources, particularly when they are used to solve real-world problems that require complex fitness evaluations. Beside the lack of resources, scientists face another problem: the absence of the required expertise to adapt applications for parallel and distributed computing models. Moreover, the computing power of PCs is frequently underused at institutions, as desktops are usually devoted to administrative tasks. Therefore, the proposal in this work consists of providing a framework that allows researchers to massively deploy EA experiments by exploiting the computing power of their instituions' PCs by setting up a Desktop Grid System based on the BOINC middleware. This paper presents a new model for running unmodified applications within BOINC with a web-based centralized management system for available resources. Thanks to this proposal, researchers can run scientific applications without modifying the application's source code, and at the same time manage thousands of computers from a single web page. Summarizing, this model allows the creation of on-demand customized execution environments within BOINC that can be used to harness unused computational resources for complex computational experiments, such as EAs. To show the performance of this model, a real-world application of Genetic Programming was used and tested through a centrally-managed desktop grid infrastructure. Results show the feasibility of the approach that has allowed researchers to generate new solutions by means of an easy to use and manage distributed system.

Compressive strength of geopolymers produced by ordinary Portland cement: Application of genetic programming for design

In the present study, compressive strength results of geopolymers produced by ordinary Portland cement (OPC) as aluminosilicate source has been modeled by gene expression programming. Six main factors including NaOH concentration, water glass to NaOH weight ratio, alkali activator to cement weight ratio, oven curing temperature, oven curing time and water curing regime each at four levels were considered for designing. A total of 32 experiments were conducted according to the L32 array proposed by the method. The gene expression programming models were constructed by 10 input parameters including NaOH concentration, water glass to NaOH weight ratio, alkali activator to cement weight ratio, oven curing Temperature, oven curing time, water curing regime, water glass content, NaOH content, Portland cement content and test trial number. The value for the output layer was the compressive strength. According to the input parameters in the models, the constructed networks were trained and tested. The results indicate that gene expression programming model is a powerful tool for predicting the compressive strength of the geopolymers in the considered range.

Application of artificial neural networks (ANNs) and genetic programming (GP) for prediction of drug release from solid lipid matrices

The aim of the present study was to develop a semi-empirical mathematical model, which is able to predict the release profiles of solid lipid extrudates of different dimensions. The development of the model was based on the application of ANNs and GP. ANNs' abilities to deal with multidimensional data were exploited. GP programming was used to determine the constants of the model function, a modified Weibull equation. Differently dimensioned extrudates consisting of diprophylline, tristearin and polyethylene glycol were produced by the use of a twin-screw extruder and their dissolution behaviour was studied. Experimentally obtained dissolution curves were compared to the calculated release profiles, derived from the semi-empirical mathematical model.

Evolving Distributed Algorithms With Genetic Programming

In this paper, we evaluate the applicability of genetic programming (GP) for the evolution of distributed algorithms. We carry out a large-scale experimental study in which we tackle three well-known problems from distributed computing with six different program representations. For this purpose, we first define a simulation environment in which phenomena such as asynchronous computation at changing speed and messages taking over each other, i.e., out-of-order message delivery, occur with high probability. Second, we define extensions and adaptations of established GP approaches (such as tree-based and linear GP) in order to make them suitable for representing distributed algorithms. Third, we introduce novel rule-based GP methods designed especially with the characteristic difficulties of evolving algorithms (such as epistasis) in mind. Based on our extensive experimental study of these approaches, we conclude that GP is indeed a viable method for evolving non-trivial, deterministic, non-approximative distributed algorithms. Furthermore, one of the two rule-based approaches is shown to exhibit superior performance in most of the tasks and thus can be considered as an interesting idea also for other problem domains.

Comparisons of VAR Model and Models Created by Genetic Programming in Consumer Price Index Prediction in Vietnam

In this paper, we present an application of Genetic Programming (GP) to Vietnamese CPI in?ation one-step prediction problem. This is a new approach in building a good forecasting model, and then applying inflation forecasts in Vietnam in current stage. The study introduces the within-sample and the out-of-samples one-step-ahead forecast errors which have positive correlation and approximate to a linear function with positive slope in prediction models by GP. We also build Vector Autoregression (VAR) model to forecast CPI in quaterly data and compare with the models created by GP. The experimental results show that the Genetic Programming can produce the prediction models having better accuracy than Vector Autoregression models. We have no relavant variables (m2, ex) of monthly data in the VAR model, so no prediction results exist to compare with models created by GP and we just forecast CPI basing on models of GP with previous data of CPI.

Evolving controllers for high-level applications on a service robot: a case study with exhibition visitor flow control

We investigate the application of simulation-based genetic programming to evolve controllers that perform high-level tasks on a service robot. As a case study, we synthesize a controller for a guide robot that manages the visitor traffic flow in an exhibition space in order to maximize the enjoyment of the visitors. We used genetic programming in a low-fidelity simulation to evolve a controller for this task, which was then transferred to a service robot. An experimental evaluation of the evolved controller in both simulation and on the actual service robot shows that it performs well compared to hand-coded heuristics, and performs comparably to a human operator.

Application of Genetic Programming for Estimation of Soil Compaction Parameters

The aim of this study is to propose two numerical models by a well-known soft computing method, Genetic Programming (GP), for the estimation of soils compaction parameters. Genetic Programming is a pattern recognition approach that has the ability of modeling the non-linear behavior of complex engineering problems. The input variables were the soil classification properties, and the outputs were the Optimum Moisture Content (OMC) and Maximum Dry Density (MDD). To provide model, a database including properties of different soils classified as CH, CI, CL, GC, GM, MH, MI, ML and SC was used. In addition, a new Multiple Linear Regression (MLR) based formula using the database, compared with the GP based model. Study results revealed that the proposed formula by GP can predict the compaction parameters of soils in a highly precise manner, and its outputs were in satisfactory conformity with real test results. Performances of the proposed models evaluated using the regression statistical analyses. The proposed formulae can be useful for the preliminary design of engineering projects and are more useful for cases with time and financial limitations.