Machine Learning

, Volume 38, Issue 1, pp 9–40

LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning

Authors

  • Ryszard S. Michalski
    • Machine Learning and Inference LaboratoryGeorge Mason University
    • Institute of Computer SciencePolish Academy of Sciences
Article

DOI: 10.1023/A:1007677805582

Cite this article as:
Michalski, R.S. Machine Learning (2000) 38: 9. doi:10.1023/A:1007677805582

Abstract

A new class of evolutionary computation processes is presented, called Learnable Evolution Model or LEM. In contrast to Darwinian-type evolution that relies on mutation, recombination, and selection operators, LEM employs machine learning to generate new populations. Specifically, in Machine Learning mode, a learning system seeks reasons why certain individuals in a population (or a collection of past populations) are superior to others in performing a designated class of tasks. These reasons, expressed as inductive hypotheses, are used to generate new populations. A remarkable property of LEM is that it is capable of quantum leaps (“insight jumps”) of the fitness function, unlike Darwinian-type evolution that typically proceeds through numerous slight improvements. In our early experimental studies, LEM significantly outperformed evolutionary computation methods used in the experiments, sometimes achieving speed-ups of two or more orders of magnitude in terms of the number of evolutionary steps. LEM has a potential for a wide range of applications, in particular, in such domains as complex optimization or search problems, engineering design, drug design, evolvable hardware, software engineering, economics, data mining, and automatic programming.

multistrategy learning genetic algorithms evolution model evolutionary computation

Copyright information

© Kluwer Academic Publishers 2000