Created by W.Langdon from gp-bibliography.bib Revision:1.5229

- @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