%0 Generic %1 Nunez:2006:msoec %A Nunez, Edwin %A Agarwal, Paul %A McBride, Marshall %A Liedel, Ron %A Owens, Claudette %D 2006 %K Evolutionary Modeling algorithms, and computation, genetic optimisation programming, simulation software techniques, tools, %T Modeling and Simulation Optimization Using Evolutionary Computation %U http://ms.ie.org/SIW_LOG/06S/06S-SIW-089.doc %X 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.

@misc{Nunez:2006:msoec, 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.}, added-at = {2008-06-19T17:46:40.000+0200}, author = {Nunez, Edwin and Agarwal, Paul and McBride, Marshall and Liedel, Ron and Owens, Claudette}, biburl = {https://www.bibsonomy.org/bibtex/2df153d36aa0675846eeb3a6c39eeb6e8/brazovayeye}, interhash = {e497ade6e561f51bae2ed75e2a1d3ff4}, intrahash = {df153d36aa0675846eeb3a6c39eeb6e8}, keywords = {Evolutionary Modeling algorithms, and computation, genetic optimisation programming, simulation software techniques, tools,}, size = {9 pages}, timestamp = {2008-06-19T17:48:37.000+0200}, title = {Modeling and Simulation Optimization Using Evolutionary Computation}, url = {http://ms.ie.org/SIW_LOG/06S/06S-SIW-089.doc}, year = 2006 }