Abstract:
LEM3 is the newest implementation of Learnable Evolution Model (LEM), a non-Darwinian evolutionary computation methodology that employs machine learning to guide evolutionary processes. Due to a deep integration of different modes of operation and the use of the advanced machine learning system AQ21, the LEM3 system is a highly efficient and effective implementation of the methodology. LEM3 supports different attribute types for describing individuals in the population, such as nominal, rank, structured, interval and ratio, which makes it applicable to a wide range of practical problems. It also implements very efficient methods for switching between different modes of operation and operators controlling the generation of new individuals. This paper describes the underlying LEM3 algorithm, results from LEM3 testing on selected benchmark function optimization problems (with the number of variables varying from 10 to 1000), and its comparison with EA, a conventional, Darwinian-type evolutionary computation program. In every experiment, without exception, LEM3 outperformed EA in terms of the evolution length (the number of fitness evaluations needed to achieved a desired solution), sometimes very significantly. It also outperformed the previous LEM2 implementation.
