224 citations · 622 across the 13 of their papers we have counts for
4 papers · 1 filter
DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess
Eli David, Nathan S. Netanyahu, Lior Wolf
We present an end-to-end learning method for chess, relying on deep neural networks. Without any a priori knowledge, in particular without any knowledge regarding the rules of ches…
Genetic Algorithms for Evolving Computer Chess Programs
Eli David, H. Jaap van den Herik, Moshe Koppel +1
This paper demonstrates the use of genetic algorithms for evolving: 1) a grandmaster-level evaluation function, and 2) a search mechanism for a chess program, the parameter values…
Genetic Algorithms for Evolving Deep Neural Networks
Eli David, Iddo Greental
In recent years, deep learning methods applying unsupervised learning to train deep layers of neural networks have achieved remarkable results in numerous fields. In the past, many…
Genetic Algorithms for Mentor-Assisted Evaluation Function Optimization
Eli David, Moshe Koppel, Nathan S. Netanyahu
In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function's parameters for computer chess. Our results show that using an appropria…