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Chess engines, computer programs designed to play chess, have become increasingly popular in recent years due to their ability to challenge human players and provide insights into the game that were previously inaccessible. However, many existing chess engines prioritize maximum performance at the expense of customizable and extensible software design, which would benefit new coming developers in the field. To address this issue, we present GoFish , a chess engine developed in Golang that achieves a balance between performance and design to serve as a modular and easy-to-extend chess engine development framework. At its core, the chess engine has two vital functions: the move search function and the position evaluation function, both of which are performance-critical. The engine has been developed with the aim of decoupling the search and evaluation modules, enabling developers to easily integrate their custom implementations for these two functions. Simultaneously, the engine automatically handles other intricate internal modules. Consequently, our proposed framework significantly reduces the development effort required to implement and compare core algorithms within the chess engine. Moreover, the engine includes default move search and evaluation implementations that have reached a playing strength comparable to that of a grandmaster. Our work is the first in the literature to present a chess engine developed in the Go programming language that strikes a balance between code performance, extendibility, and readability. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed and scalable cloud based system designed to play chess games in parallel. Games can be played between chess engines alone or between clusters created by combined chess engines. The system has a built-in mechanism that compares engines, based on Elo ranking which finally presents the strength of each tested approach. If an approach needs more computational power, the design of the system allows it to scale. The system was designed using a loosely coupled architecture approach and the master-slave pattern. It works under Unix or MacOS operating systems. In order to split chess engine processing between every CPU in the system the Akka technology with the Scala language was used while the other part was written in Java. We tested many free chess engines connected to the system by the UCI protocol supported by the proposed system. CloudAMQP is an implementation of Advanced Message Queue Protocol and was used as a message-oriented middleware. This layer was created to split games between every available processing node connected to the system. This element also contributes to greater fault tolerance. We present results of games played between many available chess engines. [ABSTRACT FROM AUTHOR]

Abstract: Here, we propose an evolutionary algorithm (i.e., evolutionary programming) for tuning the weights of a chess engine. Most of the previous work in this area has normally adopted co-evolution (i.e., tournaments among virtual players) to decide which players will pass to the following generation, depending on the outcome of each game. In contrast, our proposed method uses evolution to decide which virtual players will pass to the next generation based on the number of positions solved from a number of chess grandmaster games. Using a search depth of 1-ply, our method can solve 40.78% of the positions evaluated from chess grandmaster games (this value is higher than the one reported in the previous related work). Additionally, our method is capable of solving 53.08% of the positions using a historical mechanism that keeps a record of the “good” virtual players found during the evolutionary process. Our proposal has also been able to increase the competition level of our search engine, when playing against the program Chessmaster (grandmaster edition). Our chess engine reached a rating of 2404 points for the best virtual player with supervised learning, and a rating of 2442 points for the best virtual player with unsupervised learning. Finally, it is also worth mentioning that our results indicate that the piece material values obtained by our approach are similar to the values known from chess theory. [Copyright &y& Elsevier]