Accelerating Evolutionary Algorithms with Gaussian Process Fitness Function Models

D. Büche, N. N. Schraudolph, and P. Koumoutsakos. Accelerating Evolutionary Algorithms with Gaussian Process Fitness Function Models. IEEE Transactions on Systems, Man, and Cybernetics, C35(2):183–194, 2005.
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Abstract

We present an overview of evolutionary algorithms that use empirical models of the fitness function to accelerate convergence, distinguishing between evolution control and the surrogate approach. We describe the Gaussian process model and propose using it as an inexpensive fitness function surrogate. Implementation issues such as efficient and numerically stable computation, exploration versus exploitation, local modeling, multiple objectives and constraints, and failed evaluations are addressed. Our resulting Gaussian process optimization procedure clearly outperforms other evolutionary strategies on standard test functions as well as on a real-world problem: the optimization of stationary gas turbine compressor profiles.

BibTeX Entry

@article{BueSchKou05,
     author = {Dirk B\"uche and Nicol N. Schraudolph and Petros Koumoutsakos},
      title = {\href{http://nic.schraudolph.org/pubs/BueSchKou05.pdf}{
               Accelerating Evolutionary Algorithms with
               Gaussian Process Fitness Function Models}},
      pages = {183--194},
    journal = {{IEEE} Transactions on Systems, Man, and Cybernetics},
     volume = {C\,35},
     number =  2,
       year =  2005,
   b2h_type = {Journal Papers},
  b2h_topic = {Evolutionary Algorithms},
   b2h_note = {<a href="b2hd-BueSchKou03.html">Earlier version</a>},
   abstract = {
    We present an overview of evolutionary algorithms that use
    empirical models of the fitness function to accelerate convergence,
    distinguishing between evolution control and the surrogate approach.
    We describe the Gaussian process model and propose using it as
    an inexpensive fitness function surrogate.  Implementation issues
    such as efficient and numerically stable computation, exploration
    versus exploitation, local modeling, multiple objectives and
    constraints, and failed evaluations are addressed.  Our resulting
    Gaussian process optimization procedure clearly outperforms other
    evolutionary strategies on standard test functions as well as on
    a real-world problem: the optimization of stationary gas turbine
    compressor profiles.
}}

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