- author = "Paul Kaufmann",
- title = "Adapting Hardware Systems by Means of Multi-Objective Evolution",
- publisher = "Logos Verlag",
- year = "2013",
- address = "Berlin, Germany",
- keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, EHW, Evolvable Hardware, Multi-Objective Evolutionary Algorithms, Adaptable and Reconfigurable Architectures",
- isbn13 = "978-3-8325-3530-8",
-
URL = "
https://ris.uni-paderborn.de/record/11619",
-
URL = "
https://groups.uni-paderborn.de/agce/publications/pdfs/Kaufmann2013.pdf",
-
URL = "http://www.logos-verlag.de/cgi-bin/engbuchmid?isbn=3530&lng=deu&id=",
- size = "265 pages",
-
abstract = "Reconfigurable circuit devices have opened up a
fundamentally new way of creating adaptable systems.
Combined with artificial evolution, reconfigurable
circuits allow an elegant adaptation approach to
compensating for changes in the distribution of input
data, computational resource errors, and variations in
resource requirements. Referred to as Evolvable
Hardware (EHW), this paradigm has yielded astonishing
results for traditional engineering challenges and has
discovered intriguing design principles, which have not
yet been seen in conventional engineering.
In this thesis, we present new and fundamental work on Evolvable Hardware motivated by the insight that Evolvable Hardware needs to compensate for events with different change rates. To solve the challenge of different adaptation speeds, we propose a unified adaptation approach based on multi-objective evolution, evolving and propagating candidate solutions that are diverse in objectives that may experience radical changes.
Focusing on algorithmic aspects, we enable Cartesian Genetic Programming (CGP) model, which we are using to encode Boolean circuits, for multi-objective optimization by introducing a meaningful recombination operator. We improve the scalability of CGP by objectives scaling, periodisation of local- and global-search algorithms, and the automatic acquisition and reuse of subfunctions using age- and cone-based techniques. We validate our methods on the applications of adaptation of hardware classifiers to resource changes, recognition of muscular signals for prosthesis control and optimization of processor caches.",
