The mathematics of Petri nets
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Evaluation - Special issue: 6th international conference on modelling techniques and tools for computer performance evaluation
A genetic algorithm for generating fuzzy classification rules
Fuzzy Sets and Systems
Enhanced genetic operators for the resolution of discrete constrained optimization problems
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Petri Net Theory and the Modeling of Systems
Petri Net Theory and the Modeling of Systems
Population-Based Learning: A Method for Learning from Examples Under Resource Constraints
IEEE Transactions on Knowledge and Data Engineering
Learning with Genetic Algorithms: An Overview
Machine Learning
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic algorithms in constrained optimization
Mathematical and Computer Modelling: An International Journal
Expert Systems with Applications: An International Journal
Optimising the flow of information within a C3I network
Mathematical and Computer Modelling: An International Journal
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The problem considered is that of constructing a Petri Net model of a particular device, process, or system described by observations of its interactions with its environment. The algorithm proposed for achieving a solution employs the principles of genetic search. Also presented is a detailed investigation of its operation, thereby affording a theoretical justification of its design. The expressive and representational power of Petri Nets renders them ideally suited to the modelling of many complex event systems. As a modelling language, they directly support the intrinsically difficult concepts of concurrent and parallel activities, synchronisation of events and the distribution of various resources. However, the development of a model of a significant system is laborious and circumstances of limited knowledge of the system's internal structure compound the difficulty. By composing the perceived stimuli and responses of the system under study as a set of behavioural requirements, the Petri Net construction problem can be examined. The inherently difficult nature of this problem renders most other approaches inapplicable or inadequate; the quest for a satisfactory solution leads instead to the development of an algorithm employing genetic search technology. Built around a genetics-based machine learning architecture, the first algorithm developed shows a deficiency in its dependence on the particular order in which various options are explored. Alleviating this difficulty through a simple modification may also hold a lesson for other classifier systems.