Abstract
The application of population-based optimization algorithms in design is heavily driven by the translation and analysis of various data sets that represent a design problem; in evolutionary-based algorithms, these data sets are illustrated through two primary data streams: genes and fitness functions. The latter is frequently examined when analyzing the algorithm’s output, and the former is comparatively less so. This paper examines the role of genomic analysis in applying multi-objective evolutionary algorithms (MOEA) in design. The results demonstrate the significance of utilizing the genetic analysis to understand better the relationships between parameters used in the design problem’s formulation and differentiate between morphological differences in the algorithmic output not commonly observed through fitness-based analyses.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Technology|Architecture + Design
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
