# MuACOsm

MuACOsm: A New Mutation-Based Ant Colony Optimization Algorithm for Learning Finite-State Machines. In this paper we present MuACOsm - a new method of learning Finite-State Machines (FSM) based on Ant Colony Optimization (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. The goal is to maximize the given fitness function, which is defined on the set of all FSMs with given parameters. The new algorithm is compared with evolutionary algorithms and a genetic programming related approach on the well-known Artificial Ant problem.

## References in zbMATH (referenced in 2 articles )

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Sorted by year (- Kamali, M. Z. M.; Kumaresan, N.; Ratnavelu, Kuru: Takagi-Sugeno fuzzy modelling of some nonlinear problems using ant colony programming (2017)
- Chivilikhin, D. S.; Ulyantsev, V. I.; Shalyto, A. A.: Modified ant colony algorithm for constructing finite state machines from execution scenarios and temporal formulas (2016)