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Line 73: === Evolutionary learning === [[Evolutionary algorithm]]s in ILP use a population-based approach to evolve hypotheses, refining them through selection, crossover, and mutation. Methods like [[EvoLearner]] have been shown to outperform traditional approaches on structured machine learning benchmarks. <ref>{{cite conference \|~~last~~last1=Heindorf \|~~first~~first1=Stefan \|last2=Blübaum \|first2=Lukas \|last3=Düsterhus \|first3= Nick \|last4=Werner \|first4=Till \|last5=Golani \|first5=Varun Nandkumar \|last6=Demir \|first6=Caglar \|last7=Ngonga Ngomo \|first7=Axel-Cyrille \|title=EvoLearner: Learning Description Logics with Evolutionary Algorithms ~~\|url=https://arxiv.org/abs/2111.04879~~ \|conference=WWW \|date=2022\|arxiv=2111.04879 }}</ref> == List of implementations ==

Inductive logic programming: Difference between revisions