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conduct a random search, which ruins its learning ability. Based on our observation,
using some different levels of crossover and mutation probabilities as well as other
mutation operators did not have so much positive impact on the solution quality. In spite
of all the disadvantages above, this work clearly shows the applicability of a continuous
algorithm to solve a combinatorial optimization problem. .
For the future work, the current DE algorithm can be extended to solve some other
combinatorial/discrete optimization problems based on clusters such as resource con-
strained project scheduling (mode selection), generalized assignment problem (agent
selection), and so on. It will be also interesting to use the same representation for the
particle swarm optimization and harmony search algorithms to solve the GTSP.
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