Monte-Carlo Expression Discovery
نویسنده
چکیده
Monte-Carlo Tree Search is a general search algorithm that gives good results in games. Genetic Programming evaluates and combines trees to discover expressions that maximize a given fitness function. In this paper Monte-Carlo Tree Search is used to generate expressions that are evaluated in the same way as in Genetic Programming. Monte-Carlo Tree Search is transformed in order to search expression trees rather than lists of moves. We compare Nested Monte-Carlo Search to UCT (Upper Confidence Bounds for Trees) for various problems. Monte-Carlo Tree Search achieves state of the art results on multiple benchmark problems. The proposed approach is simple to program, does not suffer from expression growth, has a natural restart strategy to avoid local optima and is extremely easy to parallelize.
منابع مشابه
Performance comparison of four commercial GE discovery PET/CT scanners: A monte carlo study using GATE
Combined PET/CT scanners now play a major role in medicine for in vivo imaging in oncology, cardiology, neurology, and psychiatry. As the performance of a scanner depends not only on the scintillating material but also on the scanner design, with regards to the advent of newer scanners, there is a need to optimize acquisition protocols as well as to compare scanner ...
متن کاملNested Monte-Carlo Expression Discovery
Nested Monte-Carlo search is a general algorithm that gives good results in single player games. Genetic Programming evaluates and combines trees to discover expressions that maximize a given evaluation function. In this paper Nested Monte-Carlo Search is used to generate expressions that are evaluated in the same way as in Genetic Programming. Single player Nested Monte-Carlo Search is transfo...
متن کاملA profile-based deterministic sequential Monte Carlo algorithm for motif discovery
MOTIVATION Conserved motifs often represent biological significance, providing insight on biological aspects such as gene transcription regulation, biomolecular secondary structure, presence of non-coding RNAs and evolution history. With the increasing number of sequenced genomic data, faster and more accurate tools are needed to automate the process of motif discovery. RESULTS We propose a d...
متن کاملA Statistical Mechanical Approach to Combinatorial Chemistry
An analogy between combinatorial chemistry and Monte Carlo computer simulation is pursued. Examples of how to design libraries for both materials discovery and protein molecular evolution are given. For materials discovery, the concept of library redesign, or the use previous experiments to guide the design of new experiments, is introduced. For molecular evolution, examples of how to use ``bia...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- International Journal on Artificial Intelligence Tools
دوره 22 شماره
صفحات -
تاریخ انتشار 2013