Abstract
instead of randomly choosing the crossover points as
in the standard crossover operator, we use a measure
called looseness to guide the selection of crossover
points. Rather than using the genetic beam search only,
this approach uses a hybrid beam-hill climbing search
scheme in the evolutionary process. This approach is
examined and compared with the standard crossover
operator and the headless chicken crossover method on a
sequence of object classification problems. The results
suggest that this approach outperforms both the
headless chicken crossover and the standard crossover
on all of these problems.
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