Modeling and Simulation Optimization Using Evolutionary Computation

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Misc{Nunez:2006:msoec,
  author =       "Edwin Nunez and Paul Agarwal and Marshall McBride and 
                 Ron Liedel and Claudette Owens",
  title =        "Modeling and Simulation Optimization Using
                 Evolutionary Computation",
  year =         "2006",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 computation, software tools, optimisation techniques,
                 Modeling and simulation",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/06S-SIW-089.pdf",
  URL =          "http://ms.ie.org/SIW_LOG/06S/06S-SIW-089.doc",
  size =         "9 pages",
  abstract =     "Evolutionary computation (EC) is a general term
                 applied to a group of global optimisation techniques
                 whose main characteristics are inspired by biological
                 evolution. Instead of working with one possible
                 solution at a time, they usually start with a
                 population of random solutions. The initial population
                 evolves into a better set of solutions through three
                 main processes: selection, recombination, and mutation.
                 Those solutions having greater fitness are
                 preferentially selected for recombination to produce a
                 new set of possible solutions. Mutation is also used to
                 maintain diversity within the newly created solutions.
                 Through these processes, the fittest solutions transfer
                 their characteristics to a new generation of solutions.
                 By iterating over many generations, EC can find
                 solutions to many complex problems.

                 Models and simulations, especially those working as a
                 federation, can serve as the fitness function to
                 determine the value or adequacy of particular
                 solutions. In such federations, genetic programming
                 (GP) or other EC techniques can quickly find optimal or
                 near-optimal solutions for particular problems or
                 situations. The user is not required to systematically
                 search for the optimal solution; the computer
                 accomplishes that task. The tradeoff for accepting this
                 advantage is the requirement for the use of high
                 performance computing resources.

                 In this paper we briefly describe the fundamental
                 characteristics of EC. We also show some of the results
                 obtained during our research and development efforts on
                 different problems like image noise reduction and
                 discrimination of buried unexploded ordnance. We also
                 provide examples of how EC can be used with models and
                 simulations to find optimum solutions to many
                 complicated problems. This technique has great
                 potential for use with models and simulations in a
                 federated environment. The modelling and simulation
                 community needs to become more aware of these powerful
                 EC techniques so they may be applied in a wide range of
                 fields to quickly provide solutions to the war
                 fighter.

                 Distribution A. Approved for public release;
                 distribution unlimited.",
}

Genetic Programming entries for Edwin Nunez Paul Agarwal Marshall McBride Ronald Liedel Claudette Owens

Citations