Tucano Chess Engine 
This is my chess engine Tucano (Toucan in english). It is the name of a colorful bird with an oversized bill, and is popular in my country Brazil. I'm an IT professional that likes chess and programming, so I combined both with the development of the development of a chess engine. I used the information available on the internet, specially, other engines source code, such as Fruit, Crafty, Stockfish, Rodent, Olithink, Sungorus, Tscp, Ethereal, Demolito and Xiphos. Thanks to all developers that made this knowledge available! The main source of information for development of chess engines can is the Chess Programming Wiki Pages (https://www.chessprogramming.org/Main_Page). In case of any question please send me an email (alcides_schulz@hotmail.com) or stop by talkchess.com.
Tucano can be downloaded from github: https://github.com/alcides-schulz/Tucano
Starting with version 10 release, Tucano uses a neural network evaluation, which increases the engine strength. This network architecture is based on stockfish neural network. From version 10.06 and later, including official release 11.00, the network has been changed and use a new structure of 768x512x1 neurons. Also includes its own network trainer. The weights are embeeded in the file eval_nn.h.
Starting with version 11.01, it returns to version 10 archictecture, using NNUE evaluation, with incremental update of the position. For this version it will require the network file that can be download from https://github.com/alcides-schulz/TucanoNets.
Tucano is free, and distributed under the GNU General Public License (GPL). Essentially, this means that you are free to do almost exactly what you want with the program, including distributing it among your friends, making it available for download from your web site, selling it (either by itself or as part of some bigger software package), or using it as the starting point for a software project of your own.
The only real limitation is that whenever you distribute Tucano in some way, you must always include the full source code, or a pointer to where the source code can be found. If you make any changes to the source code, these changes must also be made available under the GPL.
For full details, read the copy of the GPL found in the file named copying.txt.
Notice: this is free software, there is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
Have Fun.
Alcides Schulz.
Version 8.0 and earlier supports XBoard protocol. Starting from version 9.00 it supports UCI protocol, which is the recommended protocol to use.
Starting on version 8.00, tucano supports syzygy (option SyzygyPath). It leverages Fathom from Jon Dart.
- Syzygy: endgame tables bases by Ronald de Man
- Fathom: syzygy probing tool by Jon Dart.
Tucano supports the following uci options:
option name Hash type spin default 64 min 8 max 65536
option name Threads type spin default 1 min 1 max 256
option name SyzygyPath type string default <empty>
option name Ponder type check default false
option name EvalFile type string default <empty>
tucano -hash N -threads N -syzygy_path F
-hash indicates the size of hash table, default = 64 MB, minimum: 8 MB, maximum: 65536 MB.
-threads indicates how many threads to use during search, minimum: 1, maximum: 256. Depends on how many cores your computer have.
-syzygy_path indicates the folder of syzygy endgame tablebase.
If you compile tucano you can use the command "bench" to get a signature. Just start tucano and type "bench". Signature is a number generated after searching a couple of positions to indicate you have the correct compilation. If you don't get the correct signature it means that something is wrong with the compilation process and the program may not perform correctly.
11.00: 5203395
10.00: 5734637 (with nn eval file loaded)
9.00: 21898211
8.00: 32406478
The release will come with executables for different plataforms, try to use the one that providers the most nodes per seconds. Start tucano and type the command "speed", you should see the following report:
Tucano chess engine by Alcides Schulz - 11.00 (type 'help' for information)
hash table: 64 MB, threads: 1
speed
running speed test 1 of 5...
running speed test 2 of 5...
running speed test 3 of 5...
running speed test 4 of 5...
running speed test 5 of 5...
Speed test: nodes per second average = 2232k
The main platform used for development is Windows. I try to use standard functions and make tucano portable, so it can be compiled on other platforms, but there's no warranty it will work on all of them. You can report issues with other platforms and I will try to address as possible.
Here are the commands used for compilation:
Windows (compiled using mingW version 7.2.0)
AVX2
gcc -o tucano_avx2.exe -DEGTB_SYZYGY -DTNNAVX2 -O3 -Isrc -flto -m64 -mtune=generic -s -static -Wall -Wfatal-errors -mavx2 -msse4.1 -mssse3 -msse2 -msse src\*.c src\fathom\tbprobe.c
SSE4.1
gcc -o tucano_sse4.exe -DEGTB_SYZYGY -DTNNSSE4 -O3 -Isrc -flto -m64 -mtune=generic -s -static -Wall -Wfatal-errors -msse4.1 -mssse3 -msse2 -msse src\*.c src\fathom\tbprobe.c
OLD
gcc -o tucano_old.exe -DEGTB_SYZYGY -O3 -Isrc -flto -m64 -mtune=generic -s -static -Wall -Wfatal-errors src\*.c src\fathom\tbprobe.c
Linux and ARM V8 (using src/makefile):
cd src
make <architeture>
<architecture>: avx2, sse4, old
Note: It is recommend to use AVX2 or SSE4 in order to have a good performance with neural network evaluation. The OLD version is basic and doesn't have the performance benefits of avx2 and sse4 architectures but can work for old plataforms.
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