Created by W.Langdon from gp-bibliography.bib Revision:1.4868
Throughout our research we rely on modern neo-Darwinian theory specifically, the gene-centred view of evolution to guide the design of our setup. Our evolutionary system implements strongly typed GP trees, explicitly defined introns, various mutation operators, a novel selective crossover operator, and multi-tree individuals.
Explicitly defined introns in the genome allow for information selected out of the population to be kept as a reserve for possible future use. Selective genetic operators allow us to apply additional selection pressure during the procreation stage. Multi-tree individuals allow us to evolve software components that can be integrated into existing search algorithms where they improve play level over hand-crafted baseline players.
Our results demonstrate patent improvement in play level for every game, clearly showing that GP is applicable to evolving search in board games. Results show differing levels of scalability, with the best scalability shown when using the MCTS algorithm. We also present our highly scalable EvoMCTS system designed as a scalable, easy-to-use, quick learning tool to improve the play level in games without need for any expert domain knowledge.
Pursuing the goal of general game playing (GGP) we present a system that can serve as a stepping stone on the way to general game learning (GGL), where a system can learn a game upon getting its rule set, and the human developer can improve the resulting players by supplying the learning system with relevant information about the game.",
Genetic Programming entries for Amit Benbassat