Algorithm Discovery with Monte-Carlo Search: Controlling the Size

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@InProceedings{Moudrik:2017:ICTAI,

• author = "Josef Moudrik and Tomas Kren and Roman Neruda",
• booktitle = "2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI)",
• title = "Algorithm Discovery with Monte-Carlo Search: Controlling the Size",
• year = "2017",
• pages = "390--395",
• month = nov,
• keywords = "genetic algorithms, genetic programming, MCTS, Monte-Carlo Tree Search, Parametric Polymorphism, Nested Monte-Carlo Search, UCT",
• DOI = "doi:10.1109/ICTAI.2017.00067",
• ISSN = "2375-0197",
• abstract = "The problem of automated algorithm discovery has been mainly approached by means of Genetic programming. Recently, Monte-Carlo tree search methods - well known from games - have been used for program discovery, using stack-based program representations. In this paper, we analyse the behaviour of the stack-based representations and describe an approach that provides finer control over generated program sizes and fast uniform play-outs. Our approach uses type system with parametric polymorphism to generate typed programs. We evaluate the proposed solution with two Monte-Carlo tree search algorithms, and conclude that it is a good alternative which has a better control of exploration.",
• notes = "Also known as \cite{8371970}",

}

Genetic Programming entries for Josef Moudrik Tomas Kren Roman Neruda

Citations