An Efficient Genetic Algorithm for Small Search Range Problems and Its Applications

Abstract

Genetic algorithms have been applied to many optimization and search problems and shown to be very efficient. However, the efficiency of genetic algorithms is not guaranteed in those applications where the search space is small, such as the block motion estimation in video coding applications, or equivalently the chromosome length is relatively short, less than 5 for example. Since the characteristics of these small search space applications are far away from that of the conventional search problems in which the common genetic algorithms worked well, new treatments of genetic algorithms for dealing with the small range search problems are therefore of interest. In this paper, the efficiency of the genetic operations of common genetic algorithms, such as crossover and mutation, is analyzed for this special situation. As expected, the so-obtained efficiency/performance of the genetic operations is quite different from that of their traditional 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. The control overheads of the lightweight genetic search algorithm are very low as compared with that of the conventional genetic algorithms. It is shown by simulations that many computations can be saved by applying the newly proposed algorithm while the search results are still well acceptable.