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Disadvantages of crossover techniques in genetic algorithms include the potential for losing valuable genetic material, as combining two parent solutions may lead to offspring that lack important traits. Additionally, crossover can disrupt building blocks of successful solutions, making it harder for the algorithm to converge on optimal solutions. Finally, excessive crossover can increase computational complexity and time, as it requires more evaluations of the generated offspring.

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