Noisy fitness evaluation in genetic algorithms and the dynamics of learning

A theoretical model is presented which describes selection in a genetic algorithm (GA) under a stochastic fitness measure and correctly accounts for finite population effects. Although this model describes a number of selection schemes, we only consider Boltzmann selection in detail here as results for this form of selection are particularly transparent when fitness is corrupted by additive Gaussian noise. Finite population effects are shown to be of fundamental importance in this case, as the noise has no effect in the infinite population limit. In the limit of weak selection we show how the effects of any Gaussian noise can be removed by increasing the population size appropriately. The theory is tested on two closely related problems: the one-max problem corrupted by Gaussian noise and generalization in a perceptron with binary weights. The averaged dynamics can be accurately modelled for both problems using a formalism which describes the dynamics of the GA using methods from statistical mechanics. The second problem is a simple example of a learning problem and by considering this problem we show how the accurate characterization of noise in the fitness evaluation may be relevant in machine learning. The training error (negative fitness) is the number of misclassified training examples in a batch and can be considered as a noisy version of the generalization error if an independent batch is used for each evaluation. The noise is due to the finite batch size and in the limit of large problem size and weak selection we show how the effect of this noise can be removed by increasing the population size. This allows the optimal batch size to be determined, which minimizes computation time as well as the total number of training examples required.

Fitness and novelty in evolutionary art

In this paper the effects of introducing novelty search in evolutionary art are explored. Our algorithm combines fitness and novelty metrics to frame image evolution as a multi-objective optimisation problem, promoting the creation of images that are both suitable and diverse. The method is illustrated by using two evolutionary art engines for the evolution of figurative objects and context free design grammars. The results demonstrate the ability of the algorithm to obtain a larger set of fit images compared to traditional fitness-based evolution, regardless of the engine used.

Survey of General Optical Council disciplinary and fitness to practise hearings:2001-2011

The aim of this survey was to review 187 transcripts from the United Kingdom’s General Optical Council (GOC) Disciplinary and Fitness To Practise (FTP) Committee hearings from 2001 to 2011 in order to identify common themes and thereby help practitioners to avoid the more frequently occurring pitfalls that were recorded during this period. The study covered changes in GOC FTP regulations in 2005, which involved a change from a disciplinary to a fitness to practise process. The number of cases was very small compared to the total number of optometrist and dispensing optician registrants, which was 13709 in 2001-02 rising to 18582 in 2010-11. The main findings indicated that between 2001 and 2011 there was a three times greater likelihood that male registrants versus female registrants would be brought in front of a GOC Disciplinary or FTP Committee. In terms of erasures from the GOC registers between 2001 and 2011, male registrants were also more likely to be erased than females. The male: female split for erasures between 2001 and 2011 was five: one, increasing to seven: one when considering the situation post the 2005 GOC FTP rule change. Of the cases brought before the Disciplinary and FTP Committees between 2001 and 2011, it was noted that cases implicating theft and fraud were most frequent representing 27% of hearings examined (17% involving NHS fraud and 10% theft or fraud from an employer). The examination of transcripts revealed other hearings were more complex. These hearings often had a primary reason for the investigation that highlighted further secondary concerns that also required investigation.