{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T23:43:44Z","timestamp":1773877424189,"version":"3.50.1"},"reference-count":38,"publisher":"MIT Press","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Evolutionary Computation"],"published-print":{"date-parts":[[2018,6]]},"abstract":"<jats:p>Program semantics is a promising recent research thread in Genetic Programming (GP). Over a\u00a0dozen semantic-aware search, selection, and initialization operators for GP have been proposed to date. Some of these operators are designed to exploit the geometric properties of semantic space, while others focus on making offspring effective, that is, semantically different from their parents. Only a\u00a0small fraction of previous works aimed at addressing both of these features simultaneously. In this article, we propose a\u00a0suite of competent operators that combine effectiveness with geometry for population initialization, mate selection, mutation, and crossover. We present a\u00a0theoretical rationale behind these operators and compare them experimentally to operators known from literature on symbolic regression and Boolean function synthesis benchmarks. We analyze each operator in isolation as well as verify how they fare together in an evolutionary run, concluding that the competent operators are superior on a\u00a0wide range of performance indicators, including best-of-run fitness, test-set fitness, and program\u00a0size.<\/jats:p>","DOI":"10.1162\/evco_a_00205","type":"journal-article","created":{"date-parts":[[2017,2,16]],"date-time":"2017-02-16T19:50:42Z","timestamp":1487274642000},"page":"177-212","source":"Crossref","is-referenced-by-count":23,"title":["Competent Geometric Semantic Genetic Programming for Symbolic Regression and Boolean Function Synthesis"],"prefix":"10.1162","volume":"26","author":[{"given":"Tomasz P.","family":"Pawlak","sequence":"first","affiliation":[{"name":"Institute of Computing Science, Poznan University of Technology, Pozna\u0144, Poland"}]},{"given":"Krzysztof","family":"Krawiec","sequence":"additional","affiliation":[{"name":"Institute of Computing Science, Poznan University of Technology, Pozna\u0144, Poland"}]}],"member":"281","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2008.4630784"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1007\/s10710-009-9082-5"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2009.4983099"},{"key":"B4","volume-title":"Numerical analysis","author":"Burden R.","year":"2010"},{"key":"B5","first-page":"32:193","author":"Carath\u00e9odory C.","year":"1911","journal-title":"Rendiconti del Circolo Matematico di Palermo"},{"key":"B6","first-page":"78","volume":"8154","author":"Castelli M.","year":"2013","journal-title":"Proceedings of the 16th Portuguese Conference on Artificial Intelligence"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.4996\/fireecology.1101106"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.06.037"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.01.018"},{"key":"B11","first-page":"2972","author":"Galv\u00e1n-L\u00f3pez E.","year":"2013","journal-title":"Proceedings of the IEEE Congress on Evolutionary Computation"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.1.1"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-12148-7_9"},{"key":"B15","first-page":"472","volume":"6239","author":"Jackson D.","year":"2010","journal-title":"Parallel Problem Solving from Nature"},{"key":"B16","volume-title":"100 statistical tests","author":"Kanji G","year":"1999"},{"key":"B17","volume-title":"Genetic programming: On the programming of computers by means of natural selection","author":"Koza J. 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