The LEM3 Implementation of Learnable Evolution Model and Its Testing on Complex Function Optimization Problems
J. Wojtusiak, and R. Michalski. Genetic and Evolutionary Computation Conference, GECCO 2006, Seattle, WA, (8-12 July 2006)
Abstract
Learnable Evolution Model (LEM) is a form of non-Darwinian
evolutionary computation that employs machine learning to guide
evolutionary processes. Its main novelty are new type of operators
for creating new individuals, specifically, hypothesis generation,
which learns rules indicating subareas in the search space that
likely contain the optimum, and hypothesis instantiation, which
populates these subspaces with new individuals. This paper
briefly describes the newest and most advanced implementation of
learnable evolution, LEM3, its novel features, and results from its
comparison with a conventional, Darwinian-type evolutionary
computation program (EA), a cultural evolution algorithm (CA),
and the estimation of distribution algorithm (EDA) on selected
function optimization problems (with the number of variables
varying up to 1000). In every experiment, LEM3 outperformed
the compared programs in terms of the evolution length (the
number of fitness evaluations needed to achieved a desired
solution), sometimes more than by one order of magnitude.
%0 Conference Paper
%1 wojtusiak06
%A Wojtusiak, J.
%A Michalski, R. S.
%B Genetic and Evolutionary Computation Conference, GECCO 2006
%C Seattle, WA
%D 2006
%K evolution optimization algorithm
%T The LEM3 Implementation of Learnable Evolution Model and Its Testing on Complex Function Optimization Problems
%U http://en.wikipedia.org/wiki/Learnable_Evolution_Model
%X Learnable Evolution Model (LEM) is a form of non-Darwinian
evolutionary computation that employs machine learning to guide
evolutionary processes. Its main novelty are new type of operators
for creating new individuals, specifically, hypothesis generation,
which learns rules indicating subareas in the search space that
likely contain the optimum, and hypothesis instantiation, which
populates these subspaces with new individuals. This paper
briefly describes the newest and most advanced implementation of
learnable evolution, LEM3, its novel features, and results from its
comparison with a conventional, Darwinian-type evolutionary
computation program (EA), a cultural evolution algorithm (CA),
and the estimation of distribution algorithm (EDA) on selected
function optimization problems (with the number of variables
varying up to 1000). In every experiment, LEM3 outperformed
the compared programs in terms of the evolution length (the
number of fitness evaluations needed to achieved a desired
solution), sometimes more than by one order of magnitude.
@inproceedings{wojtusiak06,
abstract = {Learnable Evolution Model (LEM) is a form of non-Darwinian
evolutionary computation that employs machine learning to guide
evolutionary processes. Its main novelty are new type of operators
for creating new individuals, specifically, hypothesis generation,
which learns rules indicating subareas in the search space that
likely contain the optimum, and hypothesis instantiation, which
populates these subspaces with new individuals. This paper
briefly describes the newest and most advanced implementation of
learnable evolution, LEM3, its novel features, and results from its
comparison with a conventional, Darwinian-type evolutionary
computation program (EA), a cultural evolution algorithm (CA),
and the estimation of distribution algorithm (EDA) on selected
function optimization problems (with the number of variables
varying up to 1000). In every experiment, LEM3 outperformed
the compared programs in terms of the evolution length (the
number of fitness evaluations needed to achieved a desired
solution), sometimes more than by one order of magnitude.},
added-at = {2006-09-19T06:48:38.000+0200},
address = {Seattle, WA},
author = {Wojtusiak, J. and Michalski, R. S.},
biburl = {https://www.bibsonomy.org/bibtex/2be0dd616fe4af4d2269accff77a7a8b1/neilernst},
booktitle = {Genetic and Evolutionary Computation Conference, GECCO 2006},
day = {8-12},
interhash = {038c9a3705d19b3d9ba632eb2694e110},
intrahash = {be0dd616fe4af4d2269accff77a7a8b1},
keywords = {evolution optimization algorithm},
month = {July },
timestamp = {2006-09-19T06:48:38.000+0200},
title = {The LEM3 Implementation of Learnable Evolution Model and Its Testing on Complex Function Optimization Problems},
url = {http://en.wikipedia.org/wiki/Learnable_Evolution_Model},
year = 2006
}