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).

== Research Groups ==

*[http://www.mli.gmu.edu Machine Learning and Inference Laboratory at George Mason University]

== Selected References ==

*Cervone G., Franzese P., Machine Learning for the Source Detection of Atmospheric Emissions, Proceedings of the 8th Conference on Artificial Intelligence Applications to Environmental Science, Code J1.7, Atlanta, GA, January 2010

*Wojtusiak, J. and Michalski, R.S., "The LEM3 Implementation of Learnable Evolution Model and Its Testing on [[complex function|Complex Function]] [[Optimization (mathematics)|Optimization]] Problems," ''Proceedings of Genetic and Evolutionary Computation Conference'', GECCO 2006, Seattle, WA, July 8-12, 2006.

*Wojtusiak, J., "Initial Study on Handling Constrained Optimization Problems in Learnable Evolution Model," ''Proceedings of The Graduate Student Workshop at Genetic and Evolutionary Computation Conference'', GECCO 2006, Seattle, WA, July 8-12, 2006.

*Jourdan, L., Corne, D., Savic, D. and Walters, G., "Preliminary Investigation of the ‘Learnable Evolution Model’ for Faster/Better Multiobjective Water Systems Design," ''Proceedings of The Third Int. Conference on Evolutionary Multi-Criterion Optimization'', EMO’05, 2005.

*Domanski, P. A., Yashar, D., Kaufman, K. and Michalski, R. S., "An Optimized Design of Finned-Tube Evaporators Using the Learnable Evolution Model," ''International Journal of Heating, Ventilating, Air-Conditioning and Refrigerating Research'', 10, 201-211, April, 2004.

*Kaufman K. and Michalski R.S., "Applying Learnable Evolution Model to Heat Exchanger Design," ''Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-2000) and Twelfth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-2000)'', Austin, TX, pp. 1014-1019, 2000.

*Cervone G., Michalski R.S., Kaufman K. A., "Experimental Validations of the Learnable Evolution Model," ''2000 Congress on Evolutionary Computation'', San Diego CA, pp 1064-1071, July 2000.

*Michalski R.S., "LEARNABLE EVOLUTION MODEL Evolutionary Processes Guided by Machine Learning," ''Machine Learning'' , 38, pp 9-40, 2000.

*Michalski, R.S., "Learnable Evolution: Combining Symbolic and Evolutionary Learning," ''Proceedings of the Fourth International Workshop on [[Multistrategy]] Learning'' (MSL'98), Desenzano del Garda, Italy, pp. 14-20, June 11-13, 1998.

== Selected Referencesreferences ==

* {{citation |last1=Cervone G|first1=P., |last2=Franzese P., |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, Atlanta, GA, |date=January 2010}}

*{{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=Wojtusiak, |first1=J., "|title=Initial Study on Handling Constrained Optimization Problems in Learnable Evolution Model," ''|journal=Proceedings of Thethe Graduate Student Workshop at Genetic and Evolutionary Computation Conference'', GECCO 2006, Seattle, WA, |date=July 8-128–12, 2006. }}

*{{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 Evolution Model' for Faster/Better Multiobjective Water Systems Design |volume=3410 |pages=841–855 |year=2005|doi=10.1007/978-3-540-31880-4_58 |citeseerx=10.1.1.73.9653 |series=Lecture Notes in Computer Science |isbn=978-3-540-24983-2 }}

*{{citation |last1=Domanski, |first1=P. A., |last2=Yashar, |first2=D., |last3=Kaufman, |first3=K. and |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, 201-211,|pages=201–211 |date=April, 2004. }}

*{{citation |last1=Kaufman |first1=K. and |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)'', Austin, TX, pp. 1014-1019,|pages=1014–1019 |year=2000. }}

*{{Cite book|last1=Cervone |first1=G. |last2=Michalski |first2=R. S. |last3=Kaufman |first3=K. A. |title=Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512) |chapter=Experimental validations of the learnable evolution model |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, pp 9-40,|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), Desenzano del Garda, Italy, pp. 14-20,|pages=14–20 |date=June 11-1311–13, 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]]