In　this　study,　we　propose　a　novel　pre-learning　approach　for　genetic　programming　(GP)　that　aims　to　investigate　the　effect　of　the　probability　of　being　selected　for　each　operator.　Furthermore,　we　present　a　technique　that　combines　chaos　theory　and　searches　for　a　relatively　good　possibility　mapping　for　each　operator　using　one-dimensional　chaotic　mapping.　We　conducted　several　sets　of　comparative　experiments　on　real-world　data　to　test　the　viability　of　the　proposal.　These　experiments　included　comparisons　with　conventional　GP,　examination　of　the　impact　of　various　chaotic　mappings　on　the　proposed　algorithm,　and　implementation　of　different　optimization　strategies　to　find　the　relative　optimal　probability　mapping.　The　experimental　results　demonstrate　that　the　proposed　method　can　achieve　better　results　than　conventional　GP　in　the　tested　dataset,　without　considering　the　total　quantitative　calculation　amount.　Through　statistical　tests,　it　has　been　proven　that　the　proposed　method　is　significantly　different　from　the　conventional　method.　However,　the　discussion　regarding　the　circumstances　under　which　the　proposed　method　can　obtain　better　results　when　the　total　calculation　amount　is　limited　is　not　yet　fully　explored　due　to　the　small-scale　nature　of　the　experiments.　Our　future　studies　will　focus　on　improving　and　fully　discussing　this　idea.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd　2024.