Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/129398
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Journal article |
Title: | Feature-based diversity optimization for problem instance classification |
Author: | Gao, W. Nallaperuma, S. Neumann, F. |
Citation: | Evolutionary Computation, 2021; 29(1):107-128 |
Publisher: | Massachusetts Institute of Technology Press (MIT Press) |
Issue Date: | 2021 |
ISSN: | 1063-6560 1530-9304 |
Statement of Responsibility: | Wanru Gao, Samadhi Nallaperuma and Frank Neumann |
Abstract: | Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Traveling Salesperson problem. In this paper, we present a general framework that is able to construct a diverse set of instances which are hard or easy for a given search heuristic. Such a diverse set is obtained by using an evolutionary algorithm for constructing hard or easy instances which are diverse with respect to different features of the underlying problem. Examining the constructed instance sets, we show that many combinations of two or three features give a good classification of the TSP instances in terms of whether they are hard to be solved by 2-OPT. |
Keywords: | Classification Combinatorial optimization Evolving Instances Feature selection Travelling Salesman Problem |
Rights: | © 2020 Massachusetts Institute of Technology |
DOI: | 10.1162/evco_a_00274 |
Grant ID: | http://purl.org/au-research/grants/arc/DP140103400 http://purl.org/au-research/grants/arc/DP190103894 |
Published version: | http://dx.doi.org/10.1162/evco_a_00274 |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.