Browsing Machine Learning and Inference Laboratory, College of Health and Human Services by Subject "evolutionary computation"

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The Development of the Inductive Database System VINLEN: A Review of Current Research

Cervone, Guido; Kaufman, Kenneth A.; Michalski, Ryszard S. (2003-06)

The recently introduced Learnable Evolution Model (LEM) represents a form of non-Darwinian evolutionary computation that is guided by a learning system. Specifically, LEM "genetically engineers" new populations via hypothesis ...
ISHED1: Applying the LEM Methodology to Heat Exchanger Design

Michalski, Ryszard S.; Kaufman, Kenneth A. (2000-01)
The LEM3 Implementation of Learnable Evolution Model and Its Testing on Complex Function Optimization Problems

Wojtusiak, Janusz; Michalski, Ryszard S. (2006-07)

Learnable Evolution Model (LEM) is a form of non-Darwinian evolutionary computation that employs machine learning to guide evolutionary processes. Its main novelty are new types of operators for creating new individuals, ...
An Optimized Design of Finned-Tube Evaporators Using the Learnable Evolution Model

Domanski, Piotr A.; Yashar, David; Kaufman, Kenneth A.; Michalski, Ryszard S. (2004-02)

Optimizing the refrigerant circuitry for a finned-tube evaporator is a daunting task for traditional exhaustive search techniques due to the extremely large number of circuitry possibilities. For this reason, more intelligent ...

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