Adapting Hardware Systems by Means of Multi-Objective Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Paul Kaufmann",
  title =        "Adapting Hardware Systems by Means of Multi-Objective
  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 =          "",
  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

                 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.",

Genetic Programming entries for Paul Kaufmann