Learnable Evolution Model

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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 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.

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

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[edit] 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 on Multistrategy Learning (MSL'98), Desenzano del Garda, Italy, pp. 14-20, June 11-13, 1998.