Learnable evolution model: Difference between revisions

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{{Original research|date=September 2007}}

The '''Learnablelearnable Evolutionevolution Modelmodel''' ('''LEM''') is a novel, non-[[Darwinian]] methodology for [[evolutionary computation]] that employs [[machine learning]] to guide the generation of new individuals ([[candidate solution|candidate problem solution]]s). Unlike standard, Darwinian-type evolutionary computation methods that use random or semi-random operators for generating new individuals (such as [[mutation (genetic algorithm)|mutation]]s and/or [[genetic recombination|recombination]]s), LEM employs hypothesis generation and instantiation operators.

The [[hypothesis generation]] operator applies a machine learning program to induce descriptions that distinguish between high-[[fitness (biology)|fitness]] and low-fitness individuals in each consecutive [[population]]. Such descriptions delineate areas in the [[Candidate solution|search space]] that most likely contain the desirable solutions. Subsequently the instantiation operator samples these areas to create new individuals.

LEM has been modified from optimization ___domain to classification ___domain by augmented LEM with ID3. (February 2013 by M. Elemam Shehab, K. Badran, M. Zaki and Gouda I. Salama.

== Selected Referencesreferences ==

*M. Elemam Shehab (February 2013),"A Generic Feature Extraction Model using Learnable Evolution Models (LEM+ID3) ", International Journal of Computer Applications,Volume 64, Number 11.

* {{citation |last1=Cervone |first1=P. |last2=Franzese |title=Machine Learning for the Source Detection of Atmospheric Emissions |journal=Proceedings of the 8th Conference on Artificial Intelligence Applications to Environmental Science, Code J1.7 |___location=Atlanta, GA |date=January 2010}}