A probabilistic and multi-objective analysis of lexicase selection and epsilon-lexicase selection
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{LaCava:EC,
-
author = "William {La Cava} and Thomas Helmuth and
Lee Spector and Jason H. Moore",
-
title = "A probabilistic and multi-objective analysis of
lexicase selection and epsilon-lexicase selection",
-
journal = "Evolutionary Computation",
-
year = "2019",
-
volume = "27",
-
number = "3",
-
pages = "377--402",
-
month = "Fall",
-
keywords = "genetic algorithms, genetic programming",
-
ISSN = "1063-6560",
-
DOI = "doi:10.1162/evco_a_00224",
-
size = "28 pages",
-
abstract = "Lexicase selection is a parent selection method that
considers training cases individually, rather than in
aggregate, when performing parent selection. Whereas
previous work has demonstrated the ability of lexicase
selection to solve difficult problems in program
synthesis and symbolic regression, the central goal of
this paper is to develop the theoretical underpinnings
that explain its performance. To this end, we derive an
analytical formula that gives the expected
probabilities of selection under lexicase selection,
given a population and its behaviour. In addition, we
expand upon the relation of lexicase selection to
many-objective optimization methods to describe the
behavior of lexicase selection, which is to select
individuals on the boundaries of Pareto fronts in
high-dimensional space. We show analytically why
lexicase selection performs more poorly for certain
sizes of population and training cases, and show why it
has been shown to perform more poorly in continuous
error spaces. To address this last concern, we propose
new variants of epsilon-lexicase selection, a method
that modifies the pass condition in lexicase selection
to allow near-elite individuals to pass cases, thereby
improving selection performance with continuous errors.
We show that epsilon-lexicase outperforms several
diversity-maintenance strategies on a number of
real-world and synthetic regression problems.",
- }
Genetic Programming entries for
William La Cava
Thomas Helmuth
Lee Spector
Jason H Moore
Citations