- author = "Jin-Suk Lim and Shin Yoo",
- title = "Field Report: Applying {Monte Carlo Tree Search} for Program Synthesis",
- booktitle = "Proceedings of the 8th International Symposium on Search Based Software Engineering, SSBSE 2016",
- year = "2016",
- editor = "Federica Sarro and Kalyanmoy Deb",
- volume = "9962",
- series = "LNCS",
- pages = "304--310",
- address = "Raleigh, North Carolina, USA",
- month = "8-10 " # oct,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, genetic improvement, MCTS, PSB1",
- isbn13 = "978-3-319-47106-8",
-
URL = "
https://coinse.github.io/projects/mctsps/",
-
URL = "https://rdcu.be/dqazC",
-
DOI = "
doi:10.1007/978-3-319-47106-8_27",
- size = "7 pages",
- abstract = "Program synthesis aims to automatically generate an executable segment of code that satisfies a given set of criteria. Genetic programming has been widely studied for program synthesis. However, it has drawbacks such as code bloats and the difficulty in finer control over the growth of programs. This paper explores the possibility of applying Monte Carlo Tree Search (MCTS) technique to general purpose program synthesis. The exploratory study applies MCTS to synthesis of six small benchmarks using Java Bytecode instructions, and compares the results to those of genetic programming. The paper discusses the major challenges and outlines the future work.",
-
notes = "cites \cite{Helmuth:2015:GECCO} Web page (Aug 2016)
says 'MCTS performs comparably to GP'.
co-located with ICSME-2016 gismo",
