Learnable evolution model: Difference between revisions

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{{Original research|date=September 2007}}

The Learnable'''learnable 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 solutionssolution]]s). Unlike standard, Darwinian-type evolutionary computation methods that use random or semi-random operators for generating new individuals (such as mutations[[mutation (genetic algorithm)|mutation]]s and/or recombinations[[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).

== ResearchSelected Groupsreferences ==

* {{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 |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. |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. 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]]

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

== Selected References ==

Wojtusiak, J. and Michalski, R.S., "The LEM3 Implementation of Learnable Evolution Model and Its Testing on Complex Function 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.

Wojtusiak, J. and Michalski, R.S., "The LEM3 System for Non-Darwinian Evolutionary Computation and Its Application to Complex Function Optimization," Reports of the Machine Learning and Inference Laboratory, MLI 05-2, George Mason University, Fairfax, VA, October, 2005.

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," Reports of the Machine Learning and Inference Laboratory, MLI 04-1, George Mason University, Fairfax, VA, February, 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.

Kaufman K., Cervone G. and Michalski R.S., "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. and Zhang, Q., "Initial Experiments with the LEM1 Learnable Evolution Model: An Application to Function Optimization and Evolvable Hardware," Reports of the Machine Learning and Inference Laboratory, MLI 99-4, George Mason University, Fairfax, VA, 1999.

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.