Abstract
Like other learning paradigms, the performance of the
genetic algorithms (GAs) is dependent on the parameter
choice, on the problem representation, and on the
fitness landscape. Accordingly, a GA can show good or
weak results even when applied on the same problem.
Following this idea, the crossover operator plays an
important role, and its study is the object of the
present paper. A mathematical analysis has led us to
construct a new form of crossover operator inspired
from genetic programming (GP) that we have already
applied in field of information retrieval. In this
paper we extend the previous results and compare the
new operator with several known crossover operators
under various experimental conditions
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