Predictive Analytics Using Genetic Programming
Keywords
Evolutionary Algorithms; Genetic Programming; NASA Shuttle
Abstract
Predictive analytics is defined as the discovery of valuable patterns and relationships in structured and/or unstructured data environments using statistical and AI techniques to develop decision making systems that calculate future outcomes. The analyst must uncover and build an initial underlying structure of the problem and then support modeling strategies to find appropriate models and abstractions to build a predictive system. The goal of these predictive systems is to calculate future outcomes (with the respective risk levels) and tendencies. This paper introduces genetic programming as a predictive modeling technique which can be the core of a predictive system. To further explain the introduced framework with genetic programming, an actual case study with the Reinforced Carbon-Carbon structures of the NASA Space Shuttle is used.
Publication Date
1-1-2017
Publication Title
Artificial Intelligence: Advances in Research and Applications
Number of Pages
171-193
Document Type
Article; Book Chapter
Personal Identifier
scopus
Copyright Status
Unknown
Socpus ID
85044640187 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85044640187
STARS Citation
Rabelo, Luis; Gutierrez, Edgar; Bhide, Sayli; and Marin, Mario, "Predictive Analytics Using Genetic Programming" (2017). Scopus Export 2015-2019. 6432.
https://stars.library.ucf.edu/scopus2015/6432