Function Optimisation Using Multiple-base Population Based Incremental Learning

| Population Based Incremental Learning (PBIL) is a stochastic search technique which combines characteristics of both the Genetic Algorithm and competitive learning. It has been shown to be a simple, yet widely eeective function optimisation strategy. Applications of PBIL to date include: selecting the weights in a neural network, image registration, lter design and feature set extraction. A generalisation of PBIL to Base-N PBIL is presented and discussed. Following this, a Multiple Base version of PBIL (MB-PBIL) is presented, which attempts to optimise a function by searching in a variety of bases. (Similar strategies have been shown to be eeective for hill-climbers.) Finally, it is shown that for the standard test functions used, MB-PBIL performed better than the standard PBIL.