Learnable evolution model: Difference between revisions

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{{Original research|date=September 2007}}

The '''learnable evolution model''' ('''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 references ==

* {{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}}

*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=Wojtusiak |first1=J. |last2=Michalski |first2=R. S. |title=Proceedings of the 8th annual conference on Genetic and evolutionary computation |chapter=The LEM3 implementation of learnable evolution model and its testing on complex function optimization problems |date=2006 |___location=Seattle, WA |doi=10.1145/1143997.1144197 |page=1281|isbn=978-1595931863 |citeseerx=10.1.1.72.2298 |s2cid=6133889 }}

* {{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}}

*{{citation |last1=Wojtusiak |first1=J. |last2title=MichalskiInitial |first2=R.Study S.on |title=TheHandling LEM3Constrained ImplementationOptimization ofProblems in Learnable Evolution Model and Its Testing on [[complex function|Complexjournal=Proceedings Function]]of [[Optimizationthe (mathematics)|Optimization]]Graduate ProblemsStudent |journal=ProceedingsWorkshop ofat Genetic and Evolutionary Computation Conference, GECCO 2006 |___location=Seattle, WA |yeardate=July 8–12, 2006 |doi=10.1145/1143997.1144197 |page=1281}}

*{{citation |last1=Jourdan |first1=L. |last2=Corne |first2=D. |last3=Savic |first3=D. |last4=Walters |first4=G. |title=Evolutionary Multi-Criterion Optimization |chapter=Preliminary Investigation of the ‘Learnable'Learnable Evolution Model’Model' for Faster/Better Multiobjective Water Systems Design |journalvolume=Proceedings3410 of|pages=841–855 the|year=2005|doi=10.1007/978-3-540-31880-4_58 Third Int|citeseerx=10.1.1.73.9653 Conference|series=Lecture onNotes Evolutionaryin Multi-CriterionComputer Optimization, EMO’05Science |yearisbn=2005978-3-540-24983-2 }}

*{{citation |last1=Wojtusiak |first1=J. |title=Initial Study on Handling Constrained Optimization Problems in Learnable Evolution Model |journal=Proceedings of the Graduate Student Workshop at Genetic and Evolutionary Computation Conference, GECCO 2006 |___location=Seattle, WA |year=July 8–12, 2006}}

*{{citation |last1=Jourdan |first1=L. |last2=Corne |first2=D. |last3=Savic |first3=D. |last4=Walters |first4=G. |title=Preliminary Investigation of the ‘Learnable Evolution Model’ for Faster/Better Multiobjective Water Systems Design |journal=Proceedings of the Third Int. Conference on Evolutionary Multi-Criterion Optimization, EMO’05 |year=2005}}

*{{citation |last1=Domanski |first1=P. A. |last2=Yashar |first2=D. |last3=Kaufman |first3=K. |last4=Michalski |first4=R. S. |title=An Optimized Design of Finned-Tube Evaporators Using the Learnable Evolution Model |journal=International Journal of Heating, Ventilating, Air-Conditioning and Refrigerating Research |volume=10 |pages=201–211 |date=April 2004}}

*{{citation |last1=Kaufman |first1=K. |last2=Michalski |first2=R. S. |title=Applying Learnable Evolution Model to Heat Exchanger Design |journal=Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-2000) and Twelfth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-2000) |___location=Austin, TX |pages=1014–1019 |year=2000}}

*{{citationCite book|last1=Cervone |first1=G. |last2=Michalski |first2=R. S. |last3=Kaufman |first3=K. A. |title=Experimental ValidationsProceedings of the Learnable Evolution Model |journal=2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512) |___locationchapter=SanExperimental validations of the learnable Diegoevolution CAmodel |pages=1064–1071 |date=July 2000|volume=2 |doi=10.1109/CEC.2000.870765 |isbn=0-7803-6375-2 |s2cid=3149132 }}

*{{citation |last1=Michalski |first1=R. S. |title=LEARNABLE EVOLUTION MODEL Evolutionary Processes Guided by Machine Learning |journal=Machine Learning |volume=38 |pages=9–40 |year=2000 |doi=10.1023/A:1007677805582|doi-access=free }}

*{{citation |last1=Michalski |first1=R .S. |title=Learnable Evolution: Combining Symbolic and Evolutionary Learning |journal=Proceedings of the Fourth International Workshop on [[Multistrategy]] Learning (MSL'98) |___location=Desenzano del Garda, Italy |pages=14–20 |yeardate=June 11–13, 1998}}

*{{citation |last1=H Yar|first1=M. |title=A survey on evolutionary computation: Methods and their applications in engineering |journal=Mod. Appl. Sci |pages=14–20 |date=June 11–13, 2016}}

[[Category:Evolutionary computation]]