QUT ePrints

Morphogenetic evolvable hardware

Lee, Justin Alexander (2006) Morphogenetic evolvable hardware. .


Evolvable hardware (EHW) uses simulated evolution to generate an electronic circuit with specific characteristics, and is generally implemented on Field Programmable Gate Arrays (FPGAs). EHW has proven to be successful at producing small novel circuits for applications such as robot control and image processing, however, traditional approaches, in which the FPGA configuration is directly encoded on the chromosome, have not scaled well with increases in problem and FPGA architecture complexity. One of the methods proposed to overcome this is the incorporation of a growth process, known as morphogenesis, into the evolutionary process. However, existing approaches have tended to abstract away the underlying architectural details, either to present a simpler virtual FPGA architecture, or a biochemical model that hides the relationship between the cellular state and the underlying hardware. By abstracting away the underlying architectural details, EHW has moved away from one of its key strengths, that being to allow evolution to discover novel solutions free of designer bias. Also, by separating the biological model from the target FPGA architecture, too many assumptions and arbitrary decisions need to be made, which are liable to lead to the growth process failing to produce the desired results. In this thesis a new approach to applying morphogenesis to gate-level FPGA- based EHW is presented, whereby circuit growth is closely tied to the underlying gate-level architecture, with circuit growth being driven largely by the state of gate-level resources of the FPGA. An investigation into the applicability of biological processes, structures and mechanisms to morphogenetic EHW (MGEHW) is conducted, and the resulting design elaborated. The developed MGEHW system is applied to solving a signal routing problem with irregular and severe constraints on routing resources. It is shown that the morphogenetic approach outperforms a traditional EHW approach using a direct encoding, and importantly, is able to scale to larger, more complex, signal routing problems without any significant increase in the number of generations required to find an optimal solution. With the success of the MGEHW system in solving primarily structural prob- lems, it is then applied to solving a combinatorial function problem, specifically a one-bit full adder, with a more complete set of FPGA resources. The results of these experiments, together with the previous experiments, has provided valuable information that when analysed has enabled the identification of the critical factors that determine the likelihood of an EHW problem being solvable. In particular this has highlighted the importance of effective fitness feedback for guiding evolution towards its desired goal. Results indicate that the gate-level morphogenetic approach is promising. The research presented here is far from complete; many avenues for future research have opened. The MGEHW system that has been developed allows further research in this area to be explored experimentally. Some of the most fruitful directions for future research are described.

Impact and interest:

Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

730 since deposited on 03 Dec 2008
107 in the past twelve months

Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 16231
Item Type: QUT Thesis (PhD)
Supervisor: Sitte, Joaquin& Geva, Shlomo
Keywords: circuit growth, evolvable hardware, evolutionary computation, gene expression, morphogenesis, reconfigurable hardware
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > School of Software Engineering & Data Communications
Department: Faculty of Information Technology
Institution: Queensland University of Technology
Copyright Owner: Copyright Justin Alexander Lee
Deposited On: 03 Dec 2008 13:59
Last Modified: 29 Oct 2011 05:45

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page