Abstract
In this paper, the effectiveness of the genetic operations of the common genetic algorithms, such as crossover and mutation, are analyzed for small search range situations. As expected, the thus-obtained efficiency/performance of the genetic operations is quite different from that of their large search range counterparts. To fill this gap, a lightweight genetic search algorithm is presented to provide an efficient way for generating near-optimal solutions for these kinds of applications.
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