Fitness Variance of Formae and Performance Prediction

Representation is widely recognised as a key determinant of performance in evolutionary computation. The development of families of representation-independentoperators allows the formulation of formal representation-independent evolutionary algorithms. These formal algorithms can be instantiated for particular search problems by selecting a suitable representation. The performance of different representations, in the context of any given formal representation-independent algorithm, can then be measured. Simple analyses suggest that fitness variance of formae (generalised schemata) for the chosen representation might act as a performance predictor for evolutionary algorithms. This hypothesis is tested and supported through studies of four different representations for the travelling sales-rep problem (TSP) in the context of both formal representation-independentgenetic algorithms and corresponding memetic algorithms.