Competitive coevolutionary algorithm decision support
Daniel Prado Sánchez, Marcos Pertierra, Erik Hemberg, Una-May O’Reilly · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2018
Using coevolutionary algorithms to find solutions to problems is a powerful technique but once solutions are identified it can be difficult for a decision maker to select a solution to deploy. ESTABLO runs competitive coevolutionary algorithm variants independently, in parallel, and then combines their test and solution results at the final generation into a compendium. From there, it re-evaluates each solution, according to three different measurements, on every test as well as on a set of unseen tests. For a decision maker, it finally identifies top solutions using various metrics and visualizes them in the context of other solutions. We demonstrate ESTABLO on a cyber security related resource allocation problem.