v5.0

This release introduces an evaluation function based on a NNUE-style architecture of input size 728, with 8 buckets of 256 hidden neurons which is chosen depending on the number of pieces left on the board. This network was trained on 125 million positions from self-play games using Weiawaga v4.0. The only new search change is the introduction of reverse futility pruning. The release also includes many minor optimizations, and is ~350 Elo stronger than Weiawaga v4.0.

v4.0

This is the first release since fully porting the engine to Rust, and so that is by far the biggest change. This release also includes multithreading for the first time via Lazy SMP, the introduction of Static Exchange Evaluation, a switch to Principal Variation Search, and a host of minor optimizations made possible by writing the engine in Rust rather than Java.