Solving Five Instances of the Artificial Ant Problem with Ant Colony Optimization
Created by W.Langdon from
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- @Article{Chivilikhin:2013:PV,
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author = "Daniil S. Chivilikhin and Vladimir I. Ulyantsev and
Anatoly A. Shalyto",
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title = "Solving Five Instances of the Artificial Ant Problem
with Ant Colony Optimization",
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journal = "IFAC Proceedings Volumes",
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volume = "46",
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number = "9",
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pages = "1043--1048",
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year = "2013",
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note = "7th IFAC Conference on Manufacturing Modelling,
Management, and Control",
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ISSN = "1474-6670",
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DOI = "doi:10.3182/20130619-3-RU-3018.00436",
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URL = "http://www.sciencedirect.com/science/article/pii/S1474667016344275",
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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.",
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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