Genetic Algorithms › Disadvantages

Population initialization spends evaluations before any offspring are produced. With a population of 20 and a budget of 300, initialization costs 20 and leaves 280 offspring evaluations, or 14 complete generations. Full enumeration uses 40320 evaluations and is feasible for this teaching instance, but it has a different budget. We compare the heuristics at 300 evaluations and account separately for the exhaustive reference and other diagnostic work.

A finite run does not certify a global optimum. Outcomes can depend on the starting population, tournament size, variation probabilities, survivor rule, and stopping budget. Testing settings also performs evaluations: report that tuning separately or include it in the application budget. A successful hand-picked crossover is not evidence that a particular parameter setting will be effective across instances.

Selection can reduce diversity. When parents are identical permutations, the order crossover below returns that same permutation, so mutation is needed for a different child. A mutated child can still be rejected by elitist survival. Different permutations can also encode the same unordered pair of tours; this distinction matters when interpreting diversity. Finally, crossover can break useful route adjacencies or change vehicle assignments, even when it always produces a valid permutation.