Abstract
genetic programming for object detection problems. In
this approach, domain independent, local region pixel
statistics are used to form three terminal sets. The
function set is constructed by the four standard
arithmetic operators and a conditional operator. A
multi-objective fitness function is constructed based
on detection rate, false alarm rate, false alarm
position and program size. This approach is applied to
three object detection problems of increasing
difficulty. The results suggest that the concentric
circular pixel statistics are more effective than the
square features for these object detection problems.
The fitness function with program size is more
effective and more efficient for these object detection
problems and the evolved genetic programs using this
fitness function are much shorter and easier to
interpret.
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