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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8–12, 2006), "The LEM3 Implementation of Learnable Evolution Model and Its Testing on Complex Function Optimization Problems", Proceedings of Genetic and ... 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) Jourdan, L ... (2005), "Preliminary Investigation of the ‘Learnable Evolution Model’ for Faster/Better Multiobjective Water Systems Design", Proceedings of the Third Int ...
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