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.
Read more about Learnable Evolution Model: Selected References
Famous quotes containing the words evolution and/or model:
“The evolution of a highly destined society must be moral; it must run in the grooves of the celestial wheels.”
—Ralph Waldo Emerson (1803–1882)
“I had a wonderful job. I worked for a big model agency in Manhattan.... When I got on the subway to go to work, it was like traveling into another world. Oh, the shops were beautiful, we had Bergdorf’s, Bendel’s, Bonwit’s, DePinna. The women wore hats and gloves. Another world. At home, it was cooking, cleaning, taking care of the kids, going to PTA, Girl Scouts. But when I got into the office, everything was different, I was different.”
—Estelle Shuster (b. c. 1923)