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DTSTART:19700927T020000
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CATEGORIES:ECS Seminars
CONTACT:Yuqi Zuo
DESCRIPTION:Symbolic regression (SR) aims to discover mathematical expressi
 ons from data that describe the relationship between input variable and th
 e output variable\,\nand genetic programming (GP) has shown strong potenti
 al for this task since it can automatically search for optimal solutions w
 ithout requiring a predefined structure of the expression. Semantic GP fur
 ther enhances GP by incorporating semantic information into the evolutiona
 ry process to provide behavior aware search guidance. However\, existing s
 emantic GP methods still face several\nchallenges\, including the issue of
  program bloat\; high-dimensional semantic space\; and requiring represent
 ative selection among semantically equivalent individuals. Therefore\, sev
 eral important aspects like bloat control\, genetic operators\, and ensemb
 le learning in semantic GP need further investigation. This study aims to 
 explore new semantic GP approaches to enhance predictive performance\, int
 erpretability\, and search effectiveness. By introducing innovative subtre
 e complexity control\, adaptive training-instance selection\, semantic-bas
 ed genetic operators\, and ensemble learning mechanisms\, the proposed met
 hods are expected to outperform state-of-the-art learning algorithms on re
 gression or symbolic regression problems.
DTEND;TZID=Pacific/Auckland:20261012T114500
DTSTAMP:20261012T065900Z
DTSTART;TZID=Pacific/Auckland:20261012T104500
LOCATION:Cotton Club\, Cotton 350
ORGANIZER:Yuqi Zuo
SUMMARY:Yuqi Zuo - Semantic Genetic Programming for Symbolic Regression
UID:seminar_ecs1440_20261006125817
URL:https://vuw.zoom.us/my/ecspostgrad  
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