A Genetic Algorithms Approach for Inverse Shortest Path Length Problems



Inverse Combinatorial Optimization has become a relevant research subject over the past decades.
In graph theory, the Inverse Shortest Path Length problem becomes relevant when we don’t have access to the real cost of the arcs and want to infer their value so that the system has a specific outcome, such as one or more shortest paths between nodes. Several approaches have been proposed to tackle this problem, relying on different methods, and several applications have been suggested. This study explores an innovative evolutionary approach relying on a genetic algorithm. Two scenarios and corresponding representations are presented and experiments are conducted to test how they react to different graph characteristics and parameters. Their behaviour and differences are thoroughly discussed. The outcome supports that evolutionary algorithms may be a viable venue to tackle Inverse Shortest Path problems.


Inverse Shortest Path, Cognitive Mapping, Genetic Algorithms, Inverse Optimization


Inverse Shortest Path, Cognitive Mapping

Related Project

COSMO - COllaborative System for Mobility Optimization


International Journal of Natural Computing Research (IJNCR), Vol. 4, #4, pp. 36-54, IGI Global, January 2015

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