This research work into Evolutionary Computing field aims at improving a dataset of algorithmic gen-erative definitions able to return an optimized ‘semi–ideal’ curve that best fits a generic reality–based profile, starting from some of its points. This paper shows GAs_Genetic Algorithms applications especially with regards to study, interpretation and definition of generic polycentric curves. Current VPL tools (Galapagos–Rhino, McNeel) allow to test Evolutionary Theories for problem solving and decision makingin architectural research field. According to a human driven approach, an operator defines GENOME, FUNCTION and FITNESS to drive the Evolutionary Solver towards optimized solutions. Some case studies from Historical/Existent Architectural Heritage are used to show how GAs can simplify the digitalization process and big data interpretation.

Genetic Algorithms for Polycentric Curves Interpretation

Emanuela Lanzara
2021-01-01

Abstract

This research work into Evolutionary Computing field aims at improving a dataset of algorithmic gen-erative definitions able to return an optimized ‘semi–ideal’ curve that best fits a generic reality–based profile, starting from some of its points. This paper shows GAs_Genetic Algorithms applications especially with regards to study, interpretation and definition of generic polycentric curves. Current VPL tools (Galapagos–Rhino, McNeel) allow to test Evolutionary Theories for problem solving and decision makingin architectural research field. According to a human driven approach, an operator defines GENOME, FUNCTION and FITNESS to drive the Evolutionary Solver towards optimized solutions. Some case studies from Historical/Existent Architectural Heritage are used to show how GAs can simplify the digitalization process and big data interpretation.
9788835125280
genetic algorithms
evolutionary computing
geometry
PolyArc
cultural heritage
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12570/29539
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