{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T03:05:03Z","timestamp":1768100703588,"version":"3.49.0"},"reference-count":28,"publisher":"MIT Press","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Evolutionary Computation"],"published-print":{"date-parts":[[2021,3]]},"abstract":"<jats:p>Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Travelling Salesperson Problem (TSP). In this article, 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.<\/jats:p>","DOI":"10.1162\/evco_a_00274","type":"journal-article","created":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T22:03:40Z","timestamp":1592431420000},"page":"107-128","source":"Crossref","is-referenced-by-count":29,"title":["Feature-Based Diversity Optimization for Problem Instance Classification"],"prefix":"10.1162","volume":"29","author":[{"given":"Wanru","family":"Gao","sequence":"first","affiliation":[{"name":"School of Information Engineering, Zhengzhou University, Zhengzhou, Henan, China"}]},{"given":"Samadhi","family":"Nallaperuma","sequence":"additional","affiliation":[{"name":"University of Cambridge, Cambridge, UK"}]},{"given":"Frank","family":"Neumann","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Adelaide, Adelaide, Australia"}]}],"member":"281","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1145\/3071178.3071342"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.14.2.132.118"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(03)00169-2"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1287\/opre.6.6.791"},{"key":"B6","first-page":"1114","author":"Eggensperger K.","year":"2015","journal-title":"Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1007\/s00453-013-9801-4"},{"key":"B8","first-page":"1128","author":"Feurer M.","year":"2015","journal-title":"Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-45823-6_81"},{"key":"B10","author":"Gunn S. R.","year":"1998","journal-title":"Support vector machines for classification and regression"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2013.10.003"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1007\/s11721-011-0059-7"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1007\/s10472-013-9341-2"},{"key":"B14","author":"Meyer D.","year":"2015","journal-title":"e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10762-2_10"},{"key":"B16","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1145\/2460239.2460253","author":"Nallaperuma S.","year":"2013","journal-title":"Foundations of Genetic Algorithms"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1145\/3205455.3205532"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1145\/3321707.3321796"},{"key":"B19","doi-asserted-by":"crossref","unstructured":"Neumann, F., and Witt, C. (2010).Bioinspired computation in combinatorial optimization: Algorithms and their computational complexity. 1st ed. New York: Springer.","DOI":"10.1007\/978-3-642-16544-3"},{"key":"B20","author":"R Core Team","year":"2015","journal-title":"R: A language and environment for statistical computing"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2015.04.022"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2011.07.006"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-13800-3_29"},{"key":"B24","first-page":"707","author":"Ulrich T.","year":"2010","journal-title":"Parallel Problem Solving from Nature"},{"key":"B25","first-page":"455","author":"Ulrich T.","year":"2010","journal-title":"Genetic and Evolutionary Computation Conference (GECCO)"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1162\/evco.2006.14.4.433"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1023\/A:1019956318069"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1613\/jair.2490"}],"container-title":["Evolutionary Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mitpressjournals.org\/doi\/pdf\/10.1162\/evco_a_00274","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T14:13:49Z","timestamp":1696256029000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/evco\/article\/29\/1\/107-128\/97344"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["10.1162\/evco_a_00274"],"URL":"https:\/\/doi.org\/10.1162\/evco_a_00274","relation":{},"ISSN":["1063-6560","1530-9304"],"issn-type":[{"value":"1063-6560","type":"print"},{"value":"1530-9304","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3]]}}}