{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,4]],"date-time":"2022-04-04T13:34:56Z","timestamp":1649079296422},"reference-count":8,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2014,9]]},"abstract":"<jats:p> One sub-field of Genetic Programming (GP) which has gained recent interest is semantic GP, in which programs are evolved by manipulating program semantics instead of program syntax. This paper introduces a new semantic GP algorithm, called SGP+, which is an extension of an existing algorithm called SGP. New crossover and mutation operators are introduced which address two of the major limitations of SGP: large program trees and reduced accuracy on high-arity problems. Experimental results on \"deceptive\" Boolean problems show that programs created by the SGP+ are 3.8 times smaller while still maintaining accuracy as good as, or better than, SGP. Additionally, a statistically significant improvement in program accuracy is observed for several high-arity Boolean problems. <\/jats:p>","DOI":"10.1142\/s1469026814500187","type":"journal-article","created":{"date-parts":[[2014,9,29]],"date-time":"2014-09-29T06:41:07Z","timestamp":1411972867000},"page":"1450018","source":"Crossref","is-referenced-by-count":0,"title":["SEMANTIC SEARCH TECHNIQUES FOR LEARNING SMALLER BOOLEAN EXPRESSION TREES IN GENETIC PROGRAMMING"],"prefix":"10.1142","volume":"13","author":[{"given":"NICHOLAS C.","family":"MILLER","sequence":"first","affiliation":[{"name":"Department of Computer Sciences, Florida Institute of Technology, 150 W. University Blvd., Melbourne, FL 32901, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"PHILIP K.","family":"CHAN","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, Florida Institute of Technology, 150 W. University Blvd., Melbourne, FL 32901, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2014,9,28]]},"reference":[{"key":"rf1","volume-title":"Genetic Programming: A Paradigm for Genetically Breeding Populations of Computer Programs to Solve Problems","author":"Koza J. R.","year":"1990"},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1007\/s10710-010-9113-2"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-20525-5_24"},{"key":"rf8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-78671-9_12"},{"key":"rf11","doi-asserted-by":"publisher","DOI":"10.1007\/s10710-010-9121-2"},{"key":"rf15","first-page":"265","volume":"34","author":"Krawiec K.","year":"2009","journal-title":"Foundation Of Computing And Decision Sciences"},{"key":"rf17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-12148-7_11"},{"key":"rf18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-29139-5_6"}],"container-title":["International Journal of Computational Intelligence and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S1469026814500187","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T18:15:16Z","timestamp":1565201716000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S1469026814500187"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,9]]},"references-count":8,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2014,9,28]]},"published-print":{"date-parts":[[2014,9]]}},"alternative-id":["10.1142\/S1469026814500187"],"URL":"https:\/\/doi.org\/10.1142\/s1469026814500187","relation":{},"ISSN":["1469-0268","1757-5885"],"issn-type":[{"value":"1469-0268","type":"print"},{"value":"1757-5885","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,9]]}}}