Population-based Incremental Learning Research Articles

Dynamic Demand Modeling Incorporating Renewable Energy Sources Using a Population-Based Optimization Method

Due to the inclusion of distributed generation (DG) in microgrids (MGs), the accelerated growth in demand, and environmental concerns, suitable management and operational strategies are imperative. The utilization of wind and solar energy has rapidly increased in MGs. However, due to the uncertainties these systems present, accurately predicting energy generation remains challenging. This necessitates modeling the system’s random variables (such as renewable resource output and possibly load demand) using appropriate and feasible methods. The primary objective of this article is to determine the optimal setpoints for renewable energy sources (RES) and all elements involved in the MG, minimizing the total operation cost. The system comprises wind turbines (WT), photovoltaic panels (PV), energy storage systems (ESS), and electric vehicles (EVs). Weibull distribution and the Hottel and Liu Jordan equations are employed to determine the potential available capacity of wind and solar energy generation, respectively. ESS is utilized to enhance MG performance. For optimal management, a comprehensive mathematical model with practical constraints for each MG element is extracted. An efficient Population-Based Incremental Learning (PBIL) metaheuristic method is proposed to solve the optimization objective in an MG, demonstrating that this energy management system optimizes and effectively coordinates DG and ESS energy generation considering economic considerations. Finally, PBIL is compared with a commonly used model, Particle Swarm Optimization (PSO), across various scenarios, analyzing and evaluating their outcomes, showcasing a reduction in operation costs.

Identification of optimal locations of adaptive traffic signal control using heuristic methods

Adaptive Traffic Signal Control (ATSC) adjusts signal timings to real-time traffic measurements, increasing operational efficiency within a network. However, ATSC is both expensive to install and operate making it infeasible to deploy at all signalized intersections within a network. This study presents a bi-level optimization framework that applies heuristic methods to identify a limited set of locations for ATSC deployment within an urban network. At the upper-level, the Population Based Incremental Learning (PBIL) algorithm is employed to generate, evaluate, learn, and update different ATSC configurations. The lower-level uses the delay-based Max-Pressure algorithm to simulate the ATSC configuration within a microsimulation platform. The study proposes improvements to the PBIL algorithm by considering constraints on the maximum number of intersections for ATSC deployment and incorporates prior information about the intersection performance (i.e., informed search). Simulation results on the traffic network of State College, PA reveal that the proposed PBIL algorithm consistently outperforms baseline methods that select locations only based on queue-lengths or delays in terms of reducing overall network travel times. The study also reveals that intersections experiencing the highest delays or longest queues are not always the best candidates for ATSC. Moreover, applying ATSC at all intersections does not always provide the best performance; in fact, ATSC applied to some locations could increase travel times by contributing additional congestion downstream. Additionally, the modified PBIL algorithm with the informed search strategy is more efficient at identifying promising solutions suggesting it can be readily applied to more generalized optimization problems.

Integrated Supply and Demand Optimization Framework in Factories using an Improved Integer form of Adaptive Population-Based Incremental Learning and a Production Simulator

Integrated Supply and Demand Optimization Framework in Factories using an Improved Integer form of Adaptive Population-Based Incremental Learning and a Production Simulator

An optimized backpropagation neural network models for the prediction of nanomaterials concentration for purification industrial wastewater

In this paper, an optimized backpropagation neural network (BPNN) prediction models have been proposed to estimate the concentration of titanium dioxide (TiO2) nanomaterials required for the removal of Acid Black 24 dye, a type of textile dye, from industrial wastewater. The research gap addressed in this study lies in the investigation of various optimization algorithms for BPNN models and their profound impact on the accuracy of nanomaterial concentration predictions, thus contributing to the advancement of nanotechnology-based wastewater treatment research and its practical applications in industrial settings. Although several studies have predicted the amount of nanomaterial needed to degrade the dyes in industrial wastewater using BPNN, few have investigated the effects of optimized BPNN and how greatly the amount of degradation can vary. Therefore, six different optimization algorithms, including genetic algorithms (GAs), ant colony optimization (ACO), particle swarm optimization (PSO), evaluation strategy (ES), biogeography-based optimization (BBO), and population-based incremental learning algorithm (PBIL), are applied to optimize and evaluate the BPNN models. Experimental data obtained from chemical laboratory experiments are used to train and test the models, with different experiments conducted for various factors like TiO2, H2SO4, NaIO4, K2S2O8, dye, and silver nitrate. The performance of the optimized BPNN models is assessed using metrics such as mean squared error (MSE), root means squared error (RMSE), and R-squared (R2). The results demonstrate the effectiveness of the proposed models, with ACO yielding the best outcomes across all models. The R2 values for the TiO2, H2SO4, NaIO4, and K2S2O8 models after optimization indicate perfect predictions, whereas the R2 values before optimization ranged from 0.888 to 0.984. PSO performs well for most models except for K2S2O8, GA improves most models except for the silver nitrate model, ES enhances only the Dye model, and PBIL does not affect the TiO2 model. Overall, the optimized BPNN models exhibit impressive accuracy in estimating the TiO2 concentration required for removing Acid Black 24 dye from industrial wastewater.

A New Parallel Cuckoo Flower Search Algorithm for Training Multi-Layer Perceptron

This paper introduces a parallel meta-heuristic algorithm called Cuckoo Flower Search (CFS). This algorithm combines the Flower Pollination Algorithm (FPA) and Cuckoo Search (CS) to train Multi-Layer Perceptron (MLP) models. The algorithm is evaluated on standard benchmark problems and its competitiveness is demonstrated against other state-of-the-art algorithms. Multiple datasets are utilized to assess the performance of CFS for MLP training. The experimental results are compared with various algorithms such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Evolutionary Search (ES), Ant Colony Optimization (ACO), and Population-based Incremental Learning (PBIL). Statistical tests are conducted to validate the superiority of the CFS algorithm in finding global optimum solutions. The results indicate that CFS achieves significantly better outcomes with a higher convergence rate when compared to the other algorithms tested. This highlights the effectiveness of CFS in solving MLP optimization problems and its potential as a competitive algorithm in the field.

Linkage Problem in Location Optimization of Dedicated Bus Lanes on a Network

Methods for identifying optimal decisions for dedicated bus lane locations (DBLs) on a network have been extensively studied in the literature. However, the impacts in relation to changes to car and bus delays of deploying a DBL on a given link largely depend on where other DBLs exist on the network. Therefore, for a network-wide location optimization or a bus lane design problem, linkages exist between decision variables. Typically used metaheuristic methods to optimize DBL locations, such as genetic algorithms (GAs), do not perform well for such problems with linkages between decision variables. To this end, this paper has two novel contributions to the literature by (a) demonstrating that the linkage problem exists, and (b) testing different heuristic algorithms that are more suitable than GAs for optimizing the locations of DBL on a network. The linkage problem in the location optimization of DBLs is demonstrated by enumerating all possible bus lane locations in a small grid network. Next, optimization algorithms that do not enumerate all possible bus lane locations that are capable of learning linkages between decision variables, namely Bayesian algorithm and a population-based incremental learning algorithm, are proposed. These algorithms are compared with two types of GAs in relation to consistency and quality of the solutions, and exploration capability. Results show that algorithms that can learn linkages between decision variables perform better than the GAs.

Hybrid Optimization Based PID Controller Design for Unstable System

PID controllers play an important function in determining tuning parameters in any process sector to deliver optimal and resilient performance for nonlinear, stable and unstable processes. The effectiveness of the presented hybrid metaheuristic algorithms for a class of time-delayed unstable systems is described in this study when applicable to the problems of PID controller and Smith PID controller. The Direct Multi Search (DMS) algorithm is utilised in this research to combine the local search ability of global heuristic algorithms to tune a PID controller for a time-delayed unstable process model. A Metaheuristics Algorithm such as, SA (Simulated Annealing), MBBO (Modified Biogeography Based Optimization), BBO (Biogeography Based Optimization), PBIL (Population Based Incremental Learning), ES (Evolution Strategy), StudGA (Stud Genetic Algorithms), PSO (Particle Swarm Optimization), StudGA (Stud Genetic Algorithms), ES (Evolution Strategy), PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) are used to tune the PID controller and Smith predictor design. The effectiveness of the suggested algorithms DMS-SA, DMS-BBO, DMS-MBBO, DMS-PBIL, DMS-StudGA, DMS-ES, DMS-ACO, and DMS-PSO for a class of dead-time structures employing PID controller and Smith predictor design controllers is illustrated using unit step set point response. When compared to other optimizations, the suggested hybrid metaheuristics approach improves the time response analysis when extended to the problem of smith predictor and PID controller designed tuning.

An artificial neural network optimized by grey wolf optimizer for prediction of hourly wind speed in Tamil Nadu, India

• The hourly wind speed prediction is an important problem. • A feed-forward (FF) multi-layer perceptron (MLP) artificial neural network (ANN) is used for prediction. • The FF MLP ANN is optimized by 9 state-of-art metaheuristic algorithms. • In the experiments, 38 years of hourly wind data belonging to 5 cities were used. • Grey Wolf Optimizer produced best results in terms of mean square error (MSE). The growing population has tremendously increased the daily energy demand all around the world. India is the second-most crowded nation in the world with approximately 1.4 billion people. New and renewable energy is on the agenda of India and in 2021 India possesses the fourth-largest installed capacity of wind power. Accurate prediction of wind speed is vital in wind farm design and operation. In this work, an hourly wind speed prediction with an artificial neural network optimized by a metaheuristics approach is conducted. A feed-forward (FF) multi-layer perceptron (MLP) artificial neural network (ANN) is used for the prediction of the hourly wind speed. In this study, 38 years of hourly wind data belonging to 5 cities (Ambur, Hosur, Kumbakonam, Nagapattinam, and Pudukottai) were used. These cities have different specific properties such as latitude, longitude, and altitude. The FF MLP ANN is optimized by 9 state-of-art metaheuristic algorithms. In this work, Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Biogeography Based Optimization (BBO), Evolutionary Strategy (ES), Genetic Algorithm (GA), Grey-Wolf-Optimizer (GWO), Population-Based Incremental Learning (PBIL), Particle Swarm Optimization (PSO), Tree-Seed Algorithm (TSA) have been used to optimize the weights of the ANN. GWO outperforms other metaheuristic algorithms in the prediction of wind speed with a FF MLP ANN model, with a success percentage rate of approximately 3% to 10,000%.

Success history based adaptive multi-objective differential evolution variants with an interval scheme for solving simultaneous topology, shape and sizing truss reliability optimisation

A general approach based on the most probable point (MPP) method for solving reliability truss optimisation with simultaneous topology, shape and sizing (TSS) design variables is developed. The design problems are solved using double loop optimisation where the inner loop is for reliability index and the probability of failure approximation is solved by Harris Hawk Optimisation (HHO). The outer loop, the main TSS truss optimisation loop, is solved by two newly developed algorithms, namely interval success history based adaptive multi-objective differential evolution (iSHAMODE) and its hybrid variant with the whale optimisation algorithm (iSHAMODE-WO). Six TSS truss optimisation problems are evaluated. The results from the proposed method for reliability approximation and First Order Second Moment (FOSM) are compared and validated with Monte Carlo Simulation (MCS). The proposed method shows more consistent and accurate results compared to FOSM. Furthermore, the efficiency of the proposed optimisation algorithms (iSHAMODE and iSHAMODE-WO) is proved; they can outperform their predecessors and several state-of-the-art algorithms including multi-objective multi-verse optimisation algorithm (MOMVO), multi-objective grasshopper optimisation algorithm (MOGOA), multi-objective dragonfly optimisation (MODA), multi-objective salp swarm algorithm (MSSA), hybridisation of real-code population-based incremental learning and differential evolution (RPBILDE), and multi-objective meta-heuristic with iterative parameter distribution estimation (MMIPDE).

A population-based incremental learning algorithm to identify optimal location of left-turn restrictions in urban grid networks

The treatment of left turns at signalized intersections drives the development of signal phasing and timing plans and also plays an important role in overall traffic network operations. Accommodating left turns allows for the most direct routeing but reduces intersection capacity, whereas restricting left turns improves capacity but requires some vehicles to travel longer distances. This paper proposes a population-based incremental learning (PBIL) algorithm to determine at which intersections left-turn restrictions should be enacted to maximize a network's operational performance. The performance of each configuration is tested in a micro-simulation environment on both perfect and imperfect square grid networks. Comparison with a partial enumeration of feasible options reveals that the PBIL algorithm is effective at identifying a near-optimal configuration of left-turn restrictions. The resulting configurations suggest that left turns should be generally restricted at intersections that carry the most flow. These intersections typically occur in the central portion of the network when demands are relatively uniform. Doing so helps to provide additional intersection capacity at the locations where it is most necessary, while minimizing the additional travel distance that is incurred due to detours caused by the left-turn restrictions. These provide insight as to how urban traffic networks might be managed to improve network efficiency by only enacting left-turn restrictions at a subset of locations.

Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning

Artificial neural networks have achieved a great success in simulating the information processing mechanism and process of neuron supervised learning, such as classification. However, traditional artificial neurons still have many problems such as slow and difficult training. This paper proposes a new dendrite neuron model (DNM), which combines metaheuristic algorithm and dendrite neuron model effectively. Eight learning algorithms including traditional backpropagation, classic evolutionary algorithms such as biogeography-based optimization, particle swarm optimization, genetic algorithm, population-based incremental learning, competitive swarm optimization, differential evolution, and state-of-the-art jSO algorithm are used for training of dendritic neuron model. The optimal combination of user-defined parameters of model has been systemically investigated, and four different datasets involving classification problem are investigated using proposed DNM. Compared with common machine learning methods such as decision tree, support vector machine, k-nearest neighbor, and artificial neural networks, dendritic neuron model trained by biogeography-based optimization has significant advantages. It has the characteristics of simple structure and low cost and can be used as a neuron model to solve practical problems with a high precision.

A Real-Coding Population-Based Incremental Learning Evolutionary Algorithm for Multi-Satellite Scheduling

With the increasing demand for earth observation in various fields, remote satellites play an important role in ground information assurance. Apparently, the effective scheduling and utilization of multi-satellite resources determine the quality and efficiency of information acquisition. In this paper, focusing on the problem of centralized multi-satellite scheduling, we establish a mathematical model of satellite scheduling with complex constraints of load and platform operation. We also propose a real-coding Population Incremental Based Learning (PBIL) algorithm to solve the multi-satellite scheduling problem. The real-coding format can greatly shorten the coding length compared to the traditional PBIL algorithm with binary coding so that the computational efficiency is improved. Additionally, we design a value probability matrix, correction coefficient and mutation operator to guide better evolution and avoid early convergence. Finally, we take some numerical examples to verify the real-coding PBIL algorithm for multi-satellite scheduling. The performance of the algorithm is analyzed by comparing it with binary-coding PBIL and the Genetic Algorithm (GA). Additionally, the influence of key parameters on algorithm performance, such as probability correction coefficient, is also analyzed.

PBIL for optimizing inception module in convolutional neural networks

Abstract Inception module is one of the most used variants in convolutional neural networks. It has a large portfolio of success cases in computer vision. In the past years, diverse inception flavours, differing in the number of branches, the size and the number of the kernels, have appeared in the scientific literature. They are proposed based on the expertise of the practitioners without any optimization process. In this work, an implementation of population-based incremental learning is proposed for automatic optimization of the hyperparameters of the inception module. This hyperparameters optimization undertakes classification of the MNIST database of handwritten digit images. This problem is widely used as a benchmark in classification, and therefore, the learned best configurations for the Inception module will be of wide use in the deep learning community. In order to reduce the carbon footprint of the optimization process, policies for reducing the redundant evaluations have been undertaken. As a consequence of this work, an evaluation of configurations of the inception module and a mechanism for optimizing hyperparameters in deep learning architectures are stated.

Reliability evaluation of a composite power system in the presence of renewable generations

Power system reliability evaluation plays a vital role in the planning and operation studies by reflecting the system safety level. In this paper, a combination of a non-sequential Monte Carlo simulation (MCS)-based model and an improved Estimation of Distribution Algorithm (EDA) is exploited for evaluating the reliability of the composite power systems considering variability and uncertainty of wind farms (WFs) and Photovoltaic (PV) units. Variability of these resources is defined as the random fluctuation of wind speed and solar irradiation caused by changes in the atmosphere, while their uncertainty results from output power forecast errors. In the proposed model, the states of traditional generating units, transmission lines, WFs, and PV units are constructed using non-sequential MCS. These states can be achieved based on the component failure probability for dispatchable traditional generators and transmission lines along with the Probability Distribution Functions (PDFs) of renewable generations. To enhance the computational efficiency of the MCS in the sampling step, the improved EDA upgraded with the Population-Based Incremental Learning (PBIL) algorithm is employed. The proposed mathematical model for reliability evaluation of composite power system is applied to the IEEE RTS 24-bus system, and numerical studies are performed under several case studies. The simulation results confirm the proficiency of the proposed method to improve the computational efficiency, while the high accuracy of reliability evaluation of the composite power system is attained.

A Modification of the PBIL Algorithm Inspired by the CMA-ES Algorithm in Discrete Knapsack Problem

The subject of this paper is the comparison of two algorithms belonging to the class of evolutionary algorithms. The first one is the well-known Population-Based Incremental Learning (PBIL) algorithm, while the second one, proposed by us, is a modification of it and based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. In the proposed Covariance Matrix Adaptation Population-Based Incremental Learning (CMA-PBIL) algorithm, the probability distribution of population is described by two parameters: the covariance matrix and the probability vector. The comparison of algorithms was performed in the discrete domain of the solution space, where we used the well-known knapsack problem in a variety of data correlations. The results obtained show that the proposed CMA-PBIL algorithm can perform better than standard PBIL in some cases. Therefore, the proposed algorithm can be a reasonable alternative to the PBIL algorithm in the discrete space domain.

Runtime Analyses of the Population-Based Univariate Estimation of Distribution Algorithms on LeadingOnes

We perform rigorous runtime analyses for the univariate marginal distribution algorithm (UMDA) and the population-based incremental learning (PBIL) Algorithm on LeadingOnes. For the UMDA, the currently known expected runtime on the function is {mathcal {O}}left( nlambda log lambda +n^2right) under an offspring population size lambda =Omega (log n) and a parent population size mu le lambda /(e(1+delta )) for any constant delta >0 (Dang and Lehre, GECCO 2015). There is no lower bound on the expected runtime under the same parameter settings. It also remains unknown whether the algorithm can still optimise the LeadingOnes function within a polynomial runtime when mu ge lambda /(e(1+delta )). In case of the PBIL, an expected runtime of {mathcal {O}}(n^{2+c}) holds for some constant c in (0,1) (Wu, Kolonko and Möhring, IEEE TEVC 2017). Despite being a generalisation of the UMDA, this upper bound is significantly asymptotically looser than the upper bound of {mathcal {O}}left( n^2right) of the UMDA for lambda =Omega (log n)cap {mathcal {O}}left( n/log nright). Furthermore, the required population size is very large, i.e., lambda =Omega (n^{1+c}). Our contributions are then threefold: (1) we show that the UMDA with mu =Omega (log n) and lambda le mu e^{1-varepsilon }/(1+delta ) for any constants varepsilon in (0,1) and 0<delta le e^{1-varepsilon }-1 requires an expected runtime of e^{Omega (mu )} on LeadingOnes, (2) an upper bound of {mathcal {O}}left( nlambda log lambda +n^2right) is shown for the PBIL, which improves the current bound {mathcal {O}}left( n^{2+c}right) by a significant factor of Theta (n^{c}), and (3) we for the first time consider the two algorithms on the LeadingOnes function in a noisy environment and obtain an expected runtime of {mathcal {O}}left( n^2right) for appropriate parameter settings. Our results emphasise that despite the independence assumption in the probabilistic models, the UMDA and the PBIL with fine-tuned parameter choices can still cope very well with variable interactions.

Optimal Location and Sizing of DGs in DC Networks Using a Hybrid Methodology Based on the PPBIL Algorithm and the VSA

In this paper, we propose a master–slave methodology to address the problem of optimal integration (location and sizing) of Distributed Generators (DGs) in Direct Current (DC) networks. This proposed methodology employs a parallel version of the Population-Based Incremental Learning (PPBIL) optimization method in the master stage to solve the location problem and the Vortex Search Algorithm (VSA) in the slave stage to solve the sizing problem. In addition, it uses the reduction of power losses as the objective function, considering all the constraints associated with the technical conditions specific to DGs and DC networks. To validate its effectiveness and robustness, we use as comparison methods, different solution methodologies that have been reported in the specialized literature, as well as two test systems (the 21 and 69-bus test systems). All simulations were performed in MATLAB. According to the results, the proposed hybrid (PPBIL–VSA) methodology provides the best trade-off between quality of the solution and processing times and exhibits an adequate repeatability every time it is executed.

Minimizing number of injection wells by binary optimization for geological carbon dioxide sequestration

Geological Carbon dioxide Sequestration (GCS) is a viable technique to reduce CO2 volume emitted to the atmosphere by injecting it into deep geological reservoirs through wells. Commercial-scale GCS needs multiple injection wells to store a large amount of CO2 exceeding some 1Mt/year. The cost for building these injection wells directly affects the project cost, therefore, the number of wells should be optimized. At the same time, feasibility of the required CO2 injection volume into the reservoir has to be evaluated. Recently, a tool has been developed by combining a stochastic optimization algorithm such as GA and PSO with a reservoir simulator for automatically optimizing the well placement. The developed tools are formulated as a continuous or mixed-integer optimization problem with many parameters such as injection/production rates and well locations as design variables. The number of wells is implicitly optimized by the injection/production rates. For example, injection wells are built when their rates are more than a user-defined one. However, proper initialization for the approach is difficult. In addition, some cases might need only the number of wells rather than precise injection rates. In this study, we propose a different approach in which optimization problem is formulated as a binary optimization whose design variables can take 0 (no well) or 1 (building an injection well) in the designated locations. Developed is a new optimization tool combining a Population-Based Incremental Learning (PBIL) for an optimization algorithm with a CO2 flow simulator TOUGH2-MP. Performance of the tool is demonstrated through case studies in a hypothetical reservoir model with an anticline structure.

WITHDRAWN: A hybrid population based incremental learning algorithm with Particle Swarm Optimization for general threshold Visual Cryptography schemes

Abstract An effortless transfer of multimedia information over the Internet is achieved due to advances in the network’s technology. Commercial identifications, similarity maps, and numerous confidential data can be transferred over the Internet. When dealing with private videos and images, security challenges must be accounted for as hackers steal information by exploiting the weak links across communication networks. There has been the development of numerous schemes for the management of the security challenges of private images and videos. The encryption of a secret image onto n shares, wherein all participants can hold one or more shares, is known as the Visual Cryptography (VC) scheme. In the probabilistic VC scheme, the secret black pixels are reconstructed incompletely thus, the display quality obtained is not consistent with human visual perception. To enhance the display quality, it is required to optimize the threshold VCS. In this work, meta heuristic algorithms, Particle Swarm Optimization (PSO), and Population-Based Incremental Learning (PBIL) are modified to optimize the VCS threshold. PSO’s abilities like simplicity, convergence speed, and optimum global searches, have made it the most frequently-employed evolutionary variant among hybrid techniques. Substitution of a population’s individuals with a set of their statistics forms the basis of the concept behind the PBIL algorithm. Proposal for a hybrid Population-Based Incremental Learning algorithm with Particle Swarm Optimization (PBIL-PSO) has been provided in this work.

Predicting Software Defects for Object-Oriented Software Using Search-based Techniques

Development without any defect is unsubstantial. Timely detection of software defects favors the proper resource utilization saving time, effort and money. With the increasing size and complexity of software, demand for accurate and efficient prediction models is increasing. Recently, search-based techniques (SBTs) have fascinated many researchers for Software Defect Prediction (SDP). The goal of this study is to conduct an empirical evaluation to assess the applicability of SBTs for predicting software defects in object-oriented (OO) softwares. In this study, 16 SBTs are exploited to build defect prediction models for 13 OO software projects. Stable performance measures — GMean, Balance and Receiver Operating Characteristic-Area Under Curve (ROC-AUC) are employed to probe into the predictive capability of developed models, taking into consideration the imbalanced nature of software datasets. Proper measures are taken to handle the stochastic behavior of SBTs. The significance of results is statistically validated using the Friedman test complied with Wilcoxon post hoc analysis. The results confirm that software defects can be detected in the early phases of software development with help of SBTs. This paper identifies the effective subset of SBTs that will aid software practitioners to timely detect the probable software defects, therefore, saving resources and bringing up good quality softwares. Eight SBTs — sUpervised Classification System (UCS), Bioinformatics-oriented hierarchical evolutionary learning (BIOHEL), CHC, Genetic Algorithm-based Classifier System with Adaptive Discretization Intervals (GA_ADI), Genetic Algorithm-based Classifier System with Intervalar Rule (GA_INT), Memetic Pittsburgh Learning Classifier System (MPLCS), Population-Based Incremental Learning (PBIL) and Steady-State Genetic Algorithm for Instance Selection (SGA) are found to be statistically good defect predictors.