File
Authors
Keywords
Many-objective genetic programming
Partial sampling
Tree structural distance
Pareto partial dominance
Subset size scheduling
Elimination of duplicates
Abstract
This paper describes a technique on an optimization of tree-structure data by of multi-objective evolutionary algorithm, or multi-objective genetic programming. GP induces bloat of the tree structure as one of the major problem. The cause of bloat is that the tree structure obtained by the crossover operator grows bigger and bigger but its evaluation does not improve. To avoid the risk of bloat, a partial sampling operator is proposed as a mating operator. The size of the tree and a structural distance are introduced into the measure of the tree-structure data as the objective functions in addition to the index of the goodness of tree structure. GP is defined as a three-objective optimization problem. SD is also applied for the ranking of parent individuals instead to the crowding distance of the conventional NSGA-II. When the index of the goodness of tree-structure data is two or more, the number of objective functions in the above problem becomes four or more. We also propose an effective many-objective EA applicable to such the many-objective GP. We focus on NSGA-II based on Pareto partial dominance (NSGA-II-PPD). NSGA-II-PPD requires beforehand a combination list of the number of objective functions to be used for Pareto partial dominance (PPD). The contents of the combination list greatly influence the optimization result. We propose to schedule a parameter r meaning the subset size of objective functions for PPD and to eliminate individuals created by the mating having the same contents as the individual of the archive set.
Publisher
Springer International Publishing
Content Type
Journal Article
Link
ISSN
25233971
Journal Title
SN Applied Sciences
Volume
1
Issue
3
Start Page
207
End Page
220
Published Date
2019-02-04
Publisher-DOI
Text Version
Author
Rights
© Springer Nature Switzerland AG 2019
Citation
Ohki, M. Multi-objective genetic programming with partial sampling and its extension to many-objective. SN Appl. Sci. (2019) 1: 207. https://doi.org/10.1007/s42452-019-0208-y. This is a post-peer-review, pre-copyedit version of an article published in SN Applied Sciences. The final authenticated version is available online at: http://dx.doi.org/10.1007/s42452-019-0208-y
Department
Faculty of Engineering/Graduate School of Engineering
Language
English