{"id":1780,"date":"2017-03-15T13:40:36","date_gmt":"2017-03-15T18:40:36","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=1780"},"modified":"2026-02-18T11:29:03","modified_gmt":"2026-02-18T16:29:03","slug":"smith2017genetic","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2017\/03\/15\/smith2017genetic\/","title":{"rendered":"Genetic Programming Based Choquet Integral for Multi-Source Fusion"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>While the Choquet integral (ChI) is a powerful parametric nonlinear aggregation function, it has limited scope and is not a universal function generator. Herein, we focus on a class of problems that are outside the scope of a single ChI. Namely, we are interested in tasks where different subsets of inputs require different ChIs. Herein, a genetic program (GP) is used to extend the ChI, referred to as GpChI hereafter, specifically in terms of compositions of ChIs and\/or arithmetic combinations of ChIs. An algorithm is put forth to learn the different GP ChIs via genetic algorithm (GA) optimization. Synthetic experiments demonstrate GpChI in a controlled fashion, i.e., we know the answer and can compare what is learned to the truth. Real-world experiments are also provided for the multi-sensor fusion of electromagnetic induction (EMI) and ground penetrating radar (GPR) for explosive hazard detection. Our multi-sensor fusion experiments show that there is utility in changing aggregation strategy per different subsets of inputs (sensors or algorithms) and fusing those results.<\/p>\n<h2>Links:<\/h2>\n<p>  <a href=\"https:\/\/github.com\/GatorSense\/Publications\/blob\/master\/Smith2017Genetic.pdf\"><img decoding=\"async\" border=\"2\" alt=\"PDF\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/pdflogo-e1482256801729.png\" height=\"50\"><\/a><\/p>\n<h2>Citation:<\/h2>\n<pre><code>R. Smith, D. Anderson, A. Zare, J. Ball, B. Alvey, J. Fairley, and S. Howington, \"Genetic Programming Based Choquet Integral for Multi-Source Fusion,\" in IEEE Int. Conf. Fuzzy Systems (FUZZ-IEEE), 2017.<\/code><\/pre>\n<pre><code>@InProceedings{Smith2017Genetic,\nTitle = {Genetic Programming Based Choquet Integral for Multi-Source Fusion},\nAuthor = {Ryan E. Smith and Derek T. Anderson and Alina Zare and John E. Ball and Brendan Alvey and Josh R Fairley and Stacy E Howington},\nBooktitle = {IEEE Int. Conf. Fuzzy Systems (FUZZ-IEEE)},\nYear = {2017},\nMonth = {Jul.}\n}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: While the Choquet integral (ChI) is a powerful parametric nonlinear aggregation function, it has limited scope and is not a universal function generator. Herein, we focus on a class of problems that are outside the scope of a single ChI. Namely, we are interested in tasks where different subsets of inputs require different ChIs. [&hellip;]<\/p>\n","protected":false},"author":28,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-templates\/single-sidebar-none.php","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"featured_post":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[17],"tags":[145,313,347,413,451,605],"class_list":["post-1780","post","type-post","status-publish","format-standard","hentry","category-conference_paper","tag-choquet-integral","tag-fusion","tag-ground-penetrating-radar","tag-landmine","tag-metal-detector","tag-radar"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1780","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/users\/28"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/comments?post=1780"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1780\/revisions"}],"predecessor-version":[{"id":14987,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1780\/revisions\/14987"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=1780"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=1780"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=1780"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}