The C programming language
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Linear analysis of genetic algorithms
Theoretical Computer Science
Theoretical Computer Science
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The UNIX Programming Environment
The UNIX Programming Environment
Evolving Chess Playing Programs
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Genetic algorithms for mentor-assisted evaluation function optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolution of an efficient search algorithm for the mate-in-N problem in chess
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
GP-EndChess: using genetic programming to evolve chess endgame players
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
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We apply an artificial intelligence method based upon a distributed simple genetic algorithm which optimizes by "learning from a mentor" to enhance the performance of the open-source, free gnu-chess program. The genetic algorithm manipulates and optimizes the collection of numerical constants within the gnu-chess program which are used to assign values to chess pieces and their positions. These are built into the code of the evaluation-function for game positions. The above collection of numerical constants within the gnu-chess program constitutes, in principle, the genotype of creatures (candidate solutions) while the compiled program using these constants constitutes the phenotype underlying the genetic algorithm used in our approach. In every generation of the genetic algorithm, the so-generated phenotypes in the current population play a fixed number of games against the original gnu-chess program in order to determine their so-defined fitness-value. After up to 17,000 hours of distributed computation-time for a single optimization on a network of 17 linux workstations, the genetic algorithm finds a chess program that shows a moderate performance improvement compared with the original gnu-chess program. What appears to be new in the approach presented here are: (a) a brute-force optimization using a mentor rather than a co-evolutionary approach is actually carried out with contemporary PC hardware, and (b) optimizations for playing white and black are carried out separately which seemingly has not been attempted by other means before.