The typical crossover operation results frequently in very drastic changes in a population of programs, which is undesirable when genetic programming is close to reaching the best solution. This paper presents a method of controlling the range of the crossover operation making the changes smoother towards the end of the programs evolution, increasing in taht way the accuracy of genetic programming.
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