Toward comparison-based adaptive operator selection

Adaptive Operator Selection (AOS) turns the impacts of the applications of variation operators into Operator Selection through a Credit Assignment mechanism. However, most Credit Assignment schemes make direct use of the fitness gain between parent and offspring. A first issue is that the Operator Selection technique that uses such kind of Credit Assignment is likely to be highly dependent on the a priori unknown bounds of the fitness function. Additionally, these bounds are likely to change along evolution, as fitness gains tend to get smaller as convergence occurs. Furthermore, and maybe more importantly, a fitness-based credit assignment forbid any invariance by monotonous transformation of the fitness, what is a usual source of robustness for comparisonbased Evolutionary Algorithms. In this context, this paper proposes two new Credit Assignment mechanisms, one inspired by the Area Under the Curve paradigm, and the other close to the Sum of Ranks. Using fitness improvement ...

• Type: Conference
• Year: 2010
• Where: GECCO
• Authors: Álvaro Fialho, Marc Schoenauer, Michèle Sebag
• Keywords: Credit Assignment, Credit Assignment Mechanisms, GECCO 2010, Operator Selection, Optimization