Evolving Finite State Machines with Embedded Genetic
Programming for Automatic Target Detection within SAR
Imagery
Proceedings of the 2000 Congress on Evolutionary
Computation CEC00, Seite 1543--1549. La Jolla Marriott Hotel La Jolla, California, USA, IEEE Press, (6-9 July 2000)Zusammenfassung
This paper presents a model comprising Finite State
Machines (FSMs) with embedded Genetic Programs (GPs)
which co-evolve to perform the task of Automatic Target
Detection (ATD). The fusion of a FSM and GPs allows for
a control structure (main program), the FSM, and
sub-programs, the GPs, to co-evolve in a symbiotic
relationship. The GP outputs along with the FSM state
transition levels are used to construct confidence
intervals that enable each pixel within the image to be
classified as either target or non-target, or to cause
a state transition to take place and further analysis
of the pixel to be performed. The algorithms produced
using this method consist of nominally four GPs, with a
typical node cardinality of less than ten, that are
executed in an order dictated by the FSM. The results
of the experimentation performed are compared to those
obtained in two independent studies of the same problem
using Kohonen Neural Networks and a two stage Genetic
Programming strategy.