Phoenix: A Self-Optimizing Chess Engine

TL;DR

This paper introduces Phoenix, a self-optimizing chess engine that uses genetic algorithms to evolve evaluation parameters, achieving master-level play with minimal initial setup and simple parameters.

Contribution

It presents a novel approach of training a chess engine from scratch using genetic algorithms and Multi-Niche Crowding to optimize evaluation functions.

Findings

Achieved International Master level after 1000 generations

Used minimal learnable parameters and simple setup

Demonstrated effectiveness of genetic algorithms in chess AI

Abstract

Since the advent of computers, many tasks which required humans to spend a lot of time and energy have been trivialized by the computers' ability to perform repetitive tasks extremely quickly. Playing chess is one such task. It was one of the first games which was `solved' using AI. With the advent of deep learning, chess playing agents can surpass human ability with relative ease. However algorithms using deep learning must learn millions of parameters. This work looks at the game of chess through the lens of genetic algorithms. We train a genetic player from scratch using only a handful of learnable parameters. We use Multi-Niche Crowding to optimize positional Value Tables (PVTs) which are used extensively in chess engines to evaluate the goodness of a position. With a very simple setup and after only 1000 generations of evolution, the player reaches the level of an International Master.