A C++ application programming interface for co-evolutionary biased random-key genetic algorithms for solution and scenario generation. (4th May 2022) - Oliveira, Beatriz Brito; Carravilla, Maria Antónia; Oliveira, José Fernando; Resende, Maurício G. C.
ABSTRACT: This paper presents a C++ application programming interface for a co-evolutionary algorithm for solution and scenario generation in stochastic problems. Based on a two-space biased random-key genetic algorithm, it involves two types of populations that are mutually impacted by the fitness calculations. In the solution population, high-quality solutions evolve, representing first-stage decisions evaluated by their performance in the face of the scenario population. The scenario population ultimately generates a diverse set of scenarios regarding their impact on the solutions. This application allows the straightforward implementation of this algorithm, where the user needs only to define the problem-dependent decoding procedure and may adjust the risk profile of the decision-maker. This paper presents the co-evolutionary algorithm and structures the interface. We also present some experiments that validate the impact of relevant features of the application.