Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Explicitly defined introns and destructive crossover in genetic programming
Advances in genetic programming
Data structures and genetic programming
Advances in genetic programming
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Emerald: Software Metrics and Models on the Desktop
IEEE Software
Assessing the applicability of fault-proneness models across object-oriented software projects
IEEE Transactions on Software Engineering
Genetic Programming Model for Software Quality Classification
HASE '01 The 6th IEEE International Symposium on High-Assurance Systems Engineering: Special Topic: Impact of Networking
Genetic Programming for Feature Discovery and Image Discrimination
Proceedings of the 5th International Conference on Genetic Algorithms
Complexity Compression and Evolution
Proceedings of the 6th International Conference on Genetic Algorithms
Fitness Causes Bloat: Mutation
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Genetic Programming-Based Decision Trees for Software Quality Classification
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Application of multivariate analysis for software fault prediction
Software Quality Control
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Effects of code growth and parsimony pressure on populations in genetic programming
Evolutionary Computation
Code growth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
An adverse interaction between crossover and restricted tree depth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Use of genetic programming to diagnose venous thromboembolism in the emergency department
Genetic Programming and Evolvable Machines
Multi filter bank approach for speaker verification based on genetic algorithm
NOLISP'07 Proceedings of the 2007 international conference on Advances in nonlinear speech processing
A novel composite model approach to improve software quality prediction
Information and Software Technology
The relationship between search based software engineering and predictive modeling
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Expert Systems with Applications: An International Journal
Random sampling technique for overfitting control in genetic programming
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
Where should we stop? an investigation on early stopping for GP learning
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Balancing learning and overfitting in genetic programming with interleaved sampling of training data
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Prediction of forest aboveground biomass: an exercise on avoiding overfitting
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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A high-assurance system is largely dependent on the quality of its underlying software. Software quality models can provide timely estimations of software quality, allowing the detection and correction of faults prior to operations. A software metrics-based quality prediction model may depict overfitting, which occurs when a prediction model has good accuracy on the training data but relatively poor accuracy on the test data. We present an approach to address the overfitting problem in the context of software quality classification models based on genetic programming (GP). The problem has not been addressed in depth for GP-based models. The presence of overfitting in a software quality classification model affects its practical usefulness, because management is interested in good performance of the model when applied to unseen software modules, i.e., generalization performance. In the process of building GP-based software quality classification models for a high-assurance telecommunications system, we observed that the GP models were prone to overfitting. We utilize a random sampling technique to reduce overfitting in our GP models. The approach has been found by many researchers as an effective method for reducing the time of a GP run. However, in our study we utilize random to reduce overfitting with the aim of improving the generalization capability of our GP models.