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-independent operators 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-independent genetic algorithms and corresponding memetic algorithms.

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