Solving Five Instances of the Artificial Ant Problem with Ant Colony Optimization

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@Article{Chivilikhin:2013:PV,

• author = "Daniil S. Chivilikhin and Vladimir I. Ulyantsev and Anatoly A. Shalyto",
• title = "Solving Five Instances of the Artificial Ant Problem with Ant Colony Optimization",
• journal = "IFAC Proceedings Volumes",
• volume = "46",
• number = "9",
• pages = "1043--1048",
• year = "2013",
• note = "7th IFAC Conference on Manufacturing Modelling, Management, and Control",
• ISSN = "1474-6670",
• DOI = "doi:10.3182/20130619-3-RU-3018.00436",
• URL = "http://www.sciencedirect.com/science/article/pii/S1474667016344275",
• abstract = "The Artificial Ant problem is a common benchmark problem often used for metaheuristic algorithm performance evaluation. The problem is to find a strategy controlling an agent (called an Artificial Ant) in a game performed on a square toroidal field. Some cells of the field contain {"}food{"} pellets, which are distributed along a certain trail. In this paper we use Finite-State Machines (FSM) for strategy representation and present a new algorithm -MuACOsm - for learning finite-state machines. The new algorithm is based on an Ant Colony Optimization algorithm (ACO) and a graph representation of the search space. We compare the new algorithm with a genetic algorithm (GA), evolutionary strategies (ES), a genetic programming related approach and reinforcement learning on five instances of the Artificial Ant Problem.",
• keywords = "genetic algorithms, genetic programming, ant colony optimization, automata-based programming, finite-state machine, learning, induction, artificial ant problem",

}

Genetic Programming entries for Daniil Chivilikhin Vladimir Ulyantsev Anatoly Abramovich Shalyto

Citations