Multi-objective Intrinsic Hardware Evolution

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

  author =       "Paul Kaufmann and Marco Platzner",
  title =        "Multi-objective Intrinsic Hardware Evolution",
  booktitle =    "Intl. Conf. Military Applications of Programmable
                 Logic Devices (MAPLD)",
  year =         "2006",
  editor =       "Richard Katz",
  pages =        "Submission 210",
  address =      "Washington, D.C, USA",
  month =        sep # " 26-28",
  organisation = "NASA",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming:Poster",
  URL =          "",
  broken =       "",
  URL =          "",
  URL =          "",
  size =         "7 pages",
  abstract =     "Computer Engineering Group A robust embedded system
                 has to adapt properly not only to changes in the
                 environment but also to changes in the available
                 resources. As an example, an autonomously moving
                 vehicle might suddenly need to assign most of its
                 computing resources to navigation, leaving less
                 resources than anticipated for other tasks.
                 Evolutionary techniques are well-suited to adapt to
                 slow changes. For rapid changes, however, the speed of
                 convergence of the evolutionary algorithm is not
                 sufficient to react properly. While we envision
                 environmental changes as rather slow, changes in the
                 available resources are considered more rapid. In our
                 project, we are concerned with intrinsically evolvable
                 digital hardware. Besides their functional quality, the
                 evolved hardware functions typically have objectives
                 such as the required logic area, the maximum operation
                 speed and the power consumption. These objectives are
                 often conflicting and cannot be optimized
                 simultaneously. A trade-off has to be found between the
                 different objectives.

                 In this paper, we present a novel approach to evolvable
                 embedded systems that is able to adapt to both slow and
                 radical changes in the environment and the system
                 state, respectively. First, a multi-objective
                 evolutionary search algorithm with a selection scheme
                 based on Pareto dominance is used to compute a set of
                 reasonable trade-offs. Then, the decision is made which
                 solution to use for the present situation. During
                 operation, the systems adapts to slowly changing
                 environmental conditions by the evolutionary search
                 process. To handle radical changes, precomputed
                 dominant solutions are stored in the system. When a
                 radical change occurs, the system switches to a
                 good-enough solution, and the online evolutionary
                 process is restarted.

                 We will present details of the Cartesian Genetic
                 Programming model used, the evolutionary technique, and
                 the evaluation of the fitness with respect to several
                 objectives. We will demonstrate our approach on two
                 classes of applications. The first class of
                 applications reveals an exact correctness measure,
                 where everything less than 100percent correctness is
                 unacceptable. For such a scenario, treating the fitness
                 as a constraint during the optimization process is a
                 viable possibility. The second class of applications
                 relies on a continuous fitness measure, such as the
                 quality of a predictor inside an image compressing
                 algorithm. For such a scenario, the functional quality
                 is best handled as an objective.",
  notes =        "SPEA2, TSPEA2, 6-parity function and a hashing


Genetic Programming entries for Paul Kaufmann Marco Platzner