A methodology for Strategy Optimization Under Uncertainty

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

@PhdThesis{moore:thesis,
  author =       "Frank William Moore",
  title =        "A methodology for Strategy Optimization Under
                 Uncertainty",
  school =       "Department of Computer Scienece and Engineering,
                 Wright State University",
  year =         "1997",
  address =      "USA",
  month =        "11 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  broken =       "http://classify.oclc.org/classify2/ClassifyDemo?owi=1864647562",
  URL =          "https://books.google.co.uk/books/about/A_Methodology_for_Strategy_Optimization.html?id=Wr9KHQAACAAJ&redir_esc=y",
  size =         "173 pages",
  abstract =     "The resulting genetic programming system evolves
                 programs that combine maneuvers with electronic
                 countermeasures to optimize aircraft survivability
                 [from anti-aircraft missile]",
  notes =        "160 Mbyte

                 In my dissertation research and related work, I evolved
                 strategies by which an aircraft could evade
                 anti-aircraft missiles. The approach I took to fitness
                 evaluation was to simulate an encounter between a
                 missile (using proportional navigation) and an aircraft
                 (controlled by stick and throttle commands issued by a
                 control program). The simulation ran at 50 Hz (typical
                 of aircraft flight control computers) Fitness was
                 equated to aircraft survivability. The training
                 population consisted of missiles launched from numerous
                 potentially lethal positions. Aggregate program fitness
                 reflected aircraft survivability against each missile
                 in the training population (i.e., program X survived 25
                 out of 50 missiles in the training population; etc.).
                 Best-of-run programs optimised survivability against
                 the training population, and were subsequently tested
                 against a large, representative test population of
                 missiles to see how well the evolved solutions
                 generalised.

                 The problem with using simulation to evaluate fitness
                 is that one has to execute each program from the
                 evolved program population over N simulated time
                 intervals, just to determine fitness against a single
                 training case. (For my missile problem, typical
                 simulated encounters lasted 20 seconds, thus entailing
                 1000 program executions PER FITNESS CASE.) So, we're
                 talking about 2-3 orders of magnitude more computation
                 than is typical for GP fitness evaluation. For the CPUs
                 available to me, it was not uncommon for a run to take
                 several days to complete. BUT the best-of-run program
                 was an embedded real-time controller that executed
                 specific aircraft manoeuvres (and, later on, deployed
                 specific countermeasures) to optimize aircraft
                 survivability. What makes that significant is the fact
                 that, for the general missile countermeasures
                 optimization problem under conditions of uncertainty
                 about missile type and/or state, NO ANALYTICAL SOLUTION
                 METHODOLOGY currently exists. I believe that by
                 combining genetic programming with sophisticated
                 simulators, we will be able to optimise programs that
                 solve a wide range of control problems for which
                 analytical solutions are difficult or impossible to
                 identify. I'd like to see GP research move away from
                 toy problems and onward to complex real-world
                 applications, and I think this approach could help
                 further that process.

                 Regards to all.

                 OCLC Work Id: 1864647562",
}

Genetic Programming entries for Frank William Moore

Citations