In this paper we propose new genetic algorithms for the tool indexing problem. Genetic
algorithms are said to be nature-inspired, in that they are modeled after the natural process of
genetic evolution. The evolution process that they model is asexual in which individuals can
potentially live forever. In this paper, we propose a genetic algorithm in which solutions are of
two genders, reproduction happens by a combination of solutions with dierent genders, and
each solution has a nite life. We compare our genetic algorithms with the best known genetic
algorithm for the tool indexing problem and report our computational experience.
Keywords: Genetic algorithm, permutation problem, crossover, mutation