A problem in the use of genetic algorithms is the premature convergence in a local optimum. Its main cause is the lack of diversity in the population due to a disproportionate relationship between exploitation and exploration. In this paper, we present heuristic crossover operators for real-coded genetic algorithms, which use the adaptation of the parents for generating the offspring. With these operators, diversity and convergence in the population may be modeled in order to avoid the premature convergence problem and to introduce good final behaviour.
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