- Learnable Evolution Model
The Learnable Evolution Model (LEM) is a novel, non-
Darwinian methodology forevolutionary computation that employsmachine learning to guide the generation of new individuals (candidate problem solutions). Unlike standard, Darwinian-type evolutionary computation methods that use random or semi-random operators for generating new individuals (such as mutations and/or recombinations), LEM employs hypothesis generation and instantiation operators. Thehypothesis generation operator applies a machine learning program to induce descriptions that distinguish between high-fitness and low-fitness individuals in each consecutivepopulation . Such descriptions delineate areas in thesearch space that most likely contain the desirable solutions. Subsequently the instantiation operator samples these areas to create new individuals.Research Groups
* [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.
*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 onMultistrategy Learning" (MSL'98), Desenzano del Garda, Italy, pp. 14-20, June 11-13, 1998.
Wikimedia Foundation. 2010.