Decoding the Architectural Genome: Multi-Objective Evolutionary Algorithms in Design

Makki, M; Navarro-Mateu, D; Showkatbakhsh, M

Decoding the Architectural Genome: Multi-Objective Evolutionary Algorithms in Design

Makki, M

Navarro-Mateu, D Showkatbakhsh, M

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Publisher:: Routledge
Publication Type:: Journal Article
Citation:: Technology|Architecture + Design, 2022, 6, (1), pp. 68-79
Issue Date:: 2022-06-06

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Field	Value	Language
dc.contributor.author	Makki, M https://orcid.org/0000-0002-8338-6134
dc.contributor.author	Navarro-Mateu, D
dc.contributor.author	Showkatbakhsh, M
dc.date.accessioned	2022-12-15T04:07:37Z
dc.date.available	2022-12-15T04:07:37Z
dc.date.issued	2022-06-06
dc.identifier.citation	Technology\|Architecture + Design, 2022, 6, (1), pp. 68-79
dc.identifier.issn	2475-143X
dc.identifier.issn	2475-143X
dc.identifier.uri	http://hdl.handle.net/10453/164430
dc.description.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.
dc.language	en
dc.publisher	Routledge
dc.relation.ispartof	Technology\|Architecture + Design
dc.relation.isbasedon	10.1080/24751448.2022.2040305
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Decoding the Architectural Genome: Multi-Objective Evolutionary Algorithms in Design
dc.type	Journal Article
utslib.citation.volume	6
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Architecture
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2022-12-15T04:07:19Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	6
utslib.citation.issue	1

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.

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http://hdl.handle.net/10453/164430