Guleng Sheri

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— The Learnable Evolution Model (LEM) was introduced by Michalski in 2000, and involves interleaved bouts of evolution and learning. Here we investigate LEM in (we think) its simplest form, using k-nearest neighbour as the 'learning' mechanism. The essence of the hybridisation is that candidate children are filtered, before evaluation, based on predictions(More)
— Inspired originally by the Learnable Evolution Model(LEM) [5], we investigate LEM(ID3), a hybrid of evolutionary search with ID3 decision tree learning. LEM(ID3) involves interleaved periods of learning and evolution, adopting the decision tree construction algorithm ID3 as the learning method, and a steady state EA as the evolution component. In the(More)
The Learnable Evolution Model (LEM) involves alternating periods of optimization and learning, performa extremely well on a range of problems, a specialises in achieveing good results in relatively few function evaluations. LEM implementations tend to use sophisticated learning strategies. Here we continue an exploration of alternative and simpler learning(More)
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