GP-EndChess: Using Genetic Programming to Evolve Chess Endgame Players

By Ami Hauptman, Moshe Sipper. 2005

78 citations recorded by the source corpus.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Riccardo Poli
2. Nicholas Freitag McPhee
3. Hervé Luga
4. William B. Langdon
5. Matthew Lai
6. Peter W. Battaglia

Citations in the corpus

• Giraffe: Using Deep Reinforcement Learning to Play Chess (2015, background): “Most attempts in this area use genetic programming [11] [12], and inductive learning [13].”
• Genetically programmed strategies for chess endgame (2006, background): “The first one is the automatic production of new heuristics for alpha-beta pruning [7], and the second is to find new evaluation functions to estimate the value of potential next moves [5, 8].”
• A Field Guide to Genetic Programming (2008, background): “Also, four of the 2005 medals were awarded for GP applications: the invention of optical lens systems (AlSakran, Koza, and Jones, 2005; Koza, Al-Sakran, and Jones, 2005), the evolution of a quantum Fourier transform algorithm (Massey, Clark, and Stepney, 2005), evolving assembly programs for Core Wa”