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One important feature of complex systems are problem domains that have manylocal minima and substructure. Biological systems manage these local minima byswitching between different subsystems depending on their environmental ordevelopmental context. Genetic Algorithms (GA) can mimic this switchingproperty as well as provide a means to overcome problem domain complexity.However, standard GA requires additional operators that will allow forlarge-scale exploration in a stochastic manner. Gradient-free heuristic searchtechniques are suitable for providing an optimal solution in the discretedomain to such single objective optimization tasks, particularly compared togradient-based methods which are noticeably slower. To do this, the authorsturn to an optimization problem from the flight scheduling domain. The authorscompare the performance of such common gradient-free heuristic searchalgorithms and propose variants of GAs. The Iterated Chaining (IC) method isalso introduced, building upon traditional chaining techniques by triggeringmultiple local searches instead of the singular action of a mutation operator.The authors will show that the use of multiple local searches can improveperformance on local stochastic searches, providing ample opportunity forapplication to a host of other problem domains. It is observed that theproposed GA variants have the least average cost across all benchmarksincluding the problem proposed and IC algorithm performs better than itsconstituents.

Flipping the switch on local exploration: Genetic Algorithms with Reversals