The recently introduced Multi-dimensional Archive of Phenotypic Elites
(MAP-Elites) is an evolutionary algorithm capable of producing a large archive
of diverse, high-performing solutions in a single run. It works by discretizing
a continuous feature space into unique regions according to the desired
discretization per dimension. While simple, this algorithm has a main drawback:
it cannot scale to high-dimensional feature spaces since the number of regions
increase exponentially with the number of dimensions. In this paper, we address
this limitation by introducing a simple extension of MAP-Elites that has a
constant, pre-defined number of regions irrespective of the dimensionality of
the feature space. Our main insight is that methods from computational geometry
could partition a high-dimensional space into well-spread geometric regions. In
particular, our algorithm uses a centroidal Voronoi tessellation (CVT) to
divide the feature space into a desired number of regions; it then places every
generated individual in its closest region, replacing a less fit one if the
region is already occupied. We demonstrate the effectiveness of the new
"CVT-MAP-Elites" algorithm in high-dimensional feature spaces through
comparisons against MAP-Elites in maze navigation and hexapod locomotion tasks.
Description
[1610.05729] Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm
%0 Generic
%1 vassiliades2016using
%A Vassiliades, Vassilis
%A Chatzilygeroudis, Konstantinos
%A Mouret, Jean-Baptiste
%D 2016
%K 2016 arxiv computing evolutionary paper
%T Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional
Archive of Phenotypic Elites Algorithm
%U http://arxiv.org/abs/1610.05729
%X The recently introduced Multi-dimensional Archive of Phenotypic Elites
(MAP-Elites) is an evolutionary algorithm capable of producing a large archive
of diverse, high-performing solutions in a single run. It works by discretizing
a continuous feature space into unique regions according to the desired
discretization per dimension. While simple, this algorithm has a main drawback:
it cannot scale to high-dimensional feature spaces since the number of regions
increase exponentially with the number of dimensions. In this paper, we address
this limitation by introducing a simple extension of MAP-Elites that has a
constant, pre-defined number of regions irrespective of the dimensionality of
the feature space. Our main insight is that methods from computational geometry
could partition a high-dimensional space into well-spread geometric regions. In
particular, our algorithm uses a centroidal Voronoi tessellation (CVT) to
divide the feature space into a desired number of regions; it then places every
generated individual in its closest region, replacing a less fit one if the
region is already occupied. We demonstrate the effectiveness of the new
"CVT-MAP-Elites" algorithm in high-dimensional feature spaces through
comparisons against MAP-Elites in maze navigation and hexapod locomotion tasks.
@misc{vassiliades2016using,
abstract = {The recently introduced Multi-dimensional Archive of Phenotypic Elites
(MAP-Elites) is an evolutionary algorithm capable of producing a large archive
of diverse, high-performing solutions in a single run. It works by discretizing
a continuous feature space into unique regions according to the desired
discretization per dimension. While simple, this algorithm has a main drawback:
it cannot scale to high-dimensional feature spaces since the number of regions
increase exponentially with the number of dimensions. In this paper, we address
this limitation by introducing a simple extension of MAP-Elites that has a
constant, pre-defined number of regions irrespective of the dimensionality of
the feature space. Our main insight is that methods from computational geometry
could partition a high-dimensional space into well-spread geometric regions. In
particular, our algorithm uses a centroidal Voronoi tessellation (CVT) to
divide the feature space into a desired number of regions; it then places every
generated individual in its closest region, replacing a less fit one if the
region is already occupied. We demonstrate the effectiveness of the new
"CVT-MAP-Elites" algorithm in high-dimensional feature spaces through
comparisons against MAP-Elites in maze navigation and hexapod locomotion tasks.},
added-at = {2018-03-17T12:36:02.000+0100},
author = {Vassiliades, Vassilis and Chatzilygeroudis, Konstantinos and Mouret, Jean-Baptiste},
biburl = {https://www.bibsonomy.org/bibtex/297e17db6d9f67a4d4d0eb10844018231/achakraborty},
description = {[1610.05729] Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm},
interhash = {a703eadd5ecc94667f3614a78f98de51},
intrahash = {97e17db6d9f67a4d4d0eb10844018231},
keywords = {2016 arxiv computing evolutionary paper},
note = {cite arxiv:1610.05729},
timestamp = {2018-03-17T12:36:02.000+0100},
title = {Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional
Archive of Phenotypic Elites Algorithm},
url = {http://arxiv.org/abs/1610.05729},
year = 2016
}