Evolvability metrics in adaptive operator selection
Jorge Alberto Soria Alcaraz, Gabriela V. Ochoa, Martín Carpio, Héctor José Puga Soberanes · 2014
Evolvability metrics gauge the potential for fitness of an individual rather than fitness itself. They measure the local characteristics of the fitness landscape surrounding a solution. In adaptive operator selection the goal is to dynamically select from a given pool the operator to apply next during the search process. An important component of these adaptive schemes is credit assignment, whereby operators are rewarded according to their observed performance. This article brings the notion of evolvability to adaptive operator selection, by proposing an autonomous search algorithm that rewards operators according to their potential for fitness rather than their immediate fitness improvement. The approach is tested within an evolutionary algorithm framework featuring several mutation operators on binary strings. Three benchmark problems of increasing difficulty, Onemax, Royal Staircase and Multiple Knapsack are considered. Experiments reveal that evolvability metrics significantly improve the performance of adaptive operator selection, when compared against standard fitness improvement metrics.The main contribution is to effectively use fitness landscape metrics to guide a self-configuring algorithm.