A methodology for Strategy Optimization Under Uncertainty

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

```@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",
month =        "11 " # aug,
keywords =     "genetic algorithms, genetic programming",
broken =       "http://classify.oclc.org/classify2/ClassifyDemo?owi=1864647562",
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