Optimizing GoTools' Search Heuristics using Genetic Algorithms
Matthew Pratola, Thomas Wolf
Abstract
GoTools is a program which solves life & death problems in the game of Go. This paper describes experiments using a Genetic Algorithm to optimize heuristic weights used by GoTools' tree-search. The complete set of heuristic weights is composed of different subgroups, each of which can be optimized with a suitable fitness function. As a useful side product, an MPI interface for FreePascal was implemented to allow the use of a parallelized fitness function running on a Beowulf cluster. The aim of this exercise is to optimize the current version of GoTools, and to make tools available in preparation of an extension of GoTools for solving open boundary life & death problems, which will introduce more heuristic parameters to be fine tuned.
Create a lesson
Related papers
Position Paper: Neurotransmitters as a Missing Dimension in Artificial Neural Networks
Yupei Li, Manuel Milling, Berrak Sisman et al.
A Metaheuristic Optimization Framework for Discrete Optimization under Strict Time Limits
Umut Çalıkyılmaz, Nitin Nayak, Sven Groppe
Benchmarking Tabular Foundation Models as Surrogates in Expensive Evolutionary Optimization
Lu Han, Jin Wang, Yuchen Li et al.
A Spatiotemporal Extension of the Neuromorphic DBSCAN Implementation
Charles P. Rizzo, James S. Plank
Machine Zygote: Causal Biparental Heredity Before Learning in a Germline--Soma Artificial Agent
Lyes Saad Saoud
Bio-Inspired Palette Evolution in Indirectly Encoded Substrates: Timescale Compatibility Shapes Activation Function Discovery
Romain Claret, Michael O'Neill, Paul Cotofrei et al.