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. 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.