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In contrast to [[Temporal Difference Learning|temporal difference methods]] such as [[Temporal Difference Learning#TDLeaf|TD-Leaf]] <ref>[[Jonathan Baxter]], [[Andrew Tridgell]], [[Lex Weaver]] ('''1998'''). ''TDLeaf(lambda): Combining Temporal Difference Learning with Game-Tree Search''. [https://www.chatbots.org/journal/australian_journal_of_intelligent_information_processing_systems/ Australian Journal of Intelligent Information Processing Systems], Vol. 5 No. 1, [http://arxiv.org/abs/cs/9901001 arXiv:cs/9901001]</ref> as used in [[KnightCap]] <ref>[[Jonathan Baxter]], [[Andrew Tridgell]], [[Lex Weaver]] ('''1998''') ''Knightcap: A chess program that learns by combining td(λ) with game-tree search''. Proceedings of the 15th International Conference on Machine Learning</ref> , where the target search is performed at subsequent time-steps, after a real move and response have been played, Meep performs various [https://en.wikipedia.org/wiki/Bootstrap_aggregating bootstrapping] techniques during training, dubbed '''RootStrap''' and '''TreeStrap''', to adjust the weights at every time-step inside either a [[Minimax|minimax]] or [[Alpha-Beta|alpha-beta]] search. With the heuristic evaluation function as linear combination of