Skip to main content

Research Repository

Advanced Search

A cross-disciplinary technology transfer for search-based evolutionary computing: From engineering design to software engineering design

Simons, Chris; Parmee, Ian

Authors

Ian Parmee



Abstract

Although object-oriented conceptual software design is difficult to learn and perform, computational tool support for the conceptual software designer is limited. In conceptual engineering design, however, computational tools exploiting interactive evolutionary computation (EC) have shown significant utility. This article investigates the cross-disciplinary technology transfer of search-based EC from engineering design to software engineering design in an attempt to provide support for the conceptual software designer. Firstly, genetic operators inspired by genetic algorithms (GAs) and evolutionary programming are evaluated for their effectiveness against a conceptual software design representation using structural cohesion as an objective fitness function. Building on this evaluation, a multi-objective GA inspired by a non-dominated Pareto sorting approach is investigated for an industrial-scale conceptual design problem. Results obtained reveal a mass of interesting and useful conceptual software design solution variants of equivalent optimality - a typical characteristic of successful multi-objective evolutionary search techniques employed in conceptual engineering design. The mass of software design solution variants produced suggests that transferring search-based technology across disciplines has significant potential to provide computationally intelligent tool support for the conceptual software designer.

Citation

Simons, C., & Parmee, I. (2007). A cross-disciplinary technology transfer for search-based evolutionary computing: From engineering design to software engineering design. Engineering Optimization, 39(5), 631-648. https://doi.org/10.1080/03052150701382974

Journal Article Type Conference Paper
Publication Date Jul 1, 2007
Journal Engineering Optimization
Print ISSN 0305-215X
Electronic ISSN 1029-0273
Publisher Taylor & Francis
Peer Reviewed Not Peer Reviewed
Volume 39
Issue 5
Pages 631-648
DOI https://doi.org/10.1080/03052150701382974
Keywords engineering design, software engineering design, search-based evolutionary computing
Public URL https://uwe-repository.worktribe.com/output/1026537
Publisher URL http://dx.doi.org/10.1080/03052150701382974