Testing transition systems: an annotated bibliography
Modeling and verification of parallel processes
Fault model-driven test derivation from finite state models: annotated bibliography
Modeling and verification of parallel processes
Ant Colony Optimization
Evolutionary computation: a unified approach
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Finding Minimum Spanning/Distances Trees by Using River Formation Dynamics
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Testing Restorable Systems by Using RFD
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
A Formal Approach to Heuristically Test Restorable Systems
ICTAC '09 Proceedings of the 6th International Colloquium on Theoretical Aspects of Computing
Using river formation dynamics to design heuristic algorithms
UC'07 Proceedings of the 6th international conference on Unconventional Computation
A Formal Approach to Heuristically Test Restorable Systems
ICTAC '09 Proceedings of the 6th International Colloquium on Theoretical Aspects of Computing
A preliminary general testing method based on genetic algorithms
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Smart data packet ad hoc routing protocol
Computer Networks: The International Journal of Computer and Telecommunications Networking
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In order to test a Finite State Machine (FSM), first we typically have to identify some short interaction sequences allowing to reach those states or transitions considered as critical . If these sequences are applied to an implementation under test (IUT), then equivalent states or transitions would be reached and observed in the implementation --- provided that the implementation were actually defined as the specification. In this paper we study how to obtain such sequences in a scenario where previous configurations can be restored at any time. In general, this feature enables sequences to reach the required parts of the machine in less time, because some repetitions can be avoided. However, finding optimal sequences is NP-hard when configurations can be restored. We use an evolutionary method, River Formation Dynamics, to heuristically solve this problem.