Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Practical genetic algorithms
Using self-organizing maps to analyze object-oriented software measures
Journal of Systems and Software
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
General fuzzy min-max neural network for clustering and classification
IEEE Transactions on Neural Networks
An empirical study of process-related attributes in segmented software cost-estimation relationships
Journal of Systems and Software
Journal of Computer Science and Technology
A hybrid heuristic approach to optimize rule-based software quality estimation models
Information and Software Technology
A granular reflex fuzzy min-max neural network for classification
IEEE Transactions on Neural Networks
Information and Software Technology
An AND-OR fuzzy neural network ship controller design
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Support vector machines for regression and applications to software quality prediction
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Locality preserving projection on source code metrics for improved software maintainability
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Simultaneous batch splitting and scheduling on identical parallel production lines
Information Sciences: an International Journal
A granular neural network: Performance analysis and application to re-granulation
International Journal of Approximate Reasoning
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Hyperbox classifiers are one of the most appealing and intuitively transparent classification schemes. As the name itself stipulates, these classifiers are based on a collection of hyperboxes--generic and highly interpretable geometric descriptors of data belonging to a given class. The hyperboxes translate into conditional statements (rules) of the form ''if feature"1 is in [a,b] and feature"2 is in [d,f] and ... and feature"n is in [w,z] then class @w'' where the intervals ([a,b],...,[w,z]) are the respective edges of the hyperbox. The proposed design process of hyperboxes comprises of two main phases. In the first phase, a collection of ''seeds'' of the hyperboxes is formed through data clustering (realized by means of the Fuzzy C-Means algorithm, FCM). In the second phase, the hyperboxes are ''grown'' (expanded) by applying mechanisms of genetic optimization (and genetic algorithm, in particular). We reveal how the underlying geometry of the hyperboxes supports an immediate interpretation of software data concerning software maintenance and dealing with rules describing a number of changes made to software modules and their linkages with various software measures (such as size of code, McCabe cyclomatic complexity, number of comments, number of characters, etc.).