Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Evolving Finite-State Machine Strategies for Protecting Resources
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
ACOhg: dealing with huge graphs
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Finite state machine induction using genetic algorithm based on testing and model checking
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
MuACOsm: a new mutation-based ant colony optimization algorithm for learning finite-state machines
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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In this paper we present a new method of learning Finite- State Machines (FSM) with the specified value of a given fitness function, which is based on an Ant Colony Optimization algorithm (ACO) and a graph representation of the search space. The input data is a set of events, a set of actions and the number of states in the target FSM and the goal is to maximize the given fitness function, which is defined on the set of all FSMs with given parameters. Comparison of the new algorithm and a genetic algorithm (GA) on benchmark problems shows that the new algorithm either outperforms GA or works just as well.