Jeannie Fitzgerald

• PhD
• Researcher at University of Limerick

This paper describes a technique which can be used with Genetic Programming (GP) to reduce implicit bias in binary classification tasks. Arbitrarily chosen class boundaries can introduce bias, but if individuals can choose their own boundaries, tailored to their function set, then their outputs are automatically scaled into a suitable range. These...

This paper explores a range of class boundary determination techniques that can be used to improve performance of Genetic Programming (GP) on binary classification tasks. These techniques involve selecting an individualised boundary threshold in order to reduce implicit bias that may be introduced through employing arbitrarily chosen values. Indivi...

In this paper we investigate a new method for improving generalization performance of Genetic Programming(GP) on Binary Classification tasks. The scheme of self adaptive, individualized genetic operators combined with adaptive tournament size is designed to provide balanced, self-adaptive exploration and exploitation. We test this scheme on several...

Historically, the quality of a solution in Genetic Programming (GP) was often assessed based on its performance on a given training sample. However, in Machine Learning, we are more interested in achieving reliable estimates of the quality of the evolving individuals on unseen data. In this paper, we propose to simulate the effect of unseen data du...

NAND Flash memory has been the fastest growing technology in the history of semiconductors and is now almost ubiquitous in the world of data storage. However, NAND devices are not error-free and the raw bit error rate (RBER) increases as devices are programmed and erase (P-E cycled). This requires the use of error correction codes (ECCs), which ope...

For some time, there has been a realisation among Genetic Programming researchers that relying on a single scalar fitness value to drive evolutionary search is no longer a satisfactory approach. Instead, efforts are being made to gain richer insights into the complexity of program behaviour. To this end, particular attention has been focused on the...

In this paper, we propose a hybrid approach to solving multi-class problems which combines evolutionary computation with elements of traditional machine learning. The method, Grammatical Evolution Machine Learning (GEML) adapts machine learning concepts from decision tree learning and clustering methods and integrates these into a Grammatical Evolu...

Wave is a novel form of semantic genetic programming which operates by optimising the residual errors of a succession of short genetic programming runs, and then producing a cumulative solution. These short genetic programming runs are called periods, and they have heterogeneous parameters. In this paper we leverage the potential of Wave's heteroge...

This paper introduces a novel evolutionary approach which can be applied to supervised, semi-supervised and unsupervised learning tasks. The method, Grammatical Evolution Machine Learning (GEML) adapts machine learning concepts from decision tree learning and clustering methods, and integrates these into a Grammatical Evolution framework. With mino...

Significant recent effort in genetic programming has focused on selecting and combining candidate solutions according to a notion of behaviour defined in semantic space and has also highlighted disadvantages of relying on a single scalar measure to capture the complexity of program performance in evolutionary search. In this paper, we take an alter...

Although very controversial in the field of evolutionary biology, the notion of evolutionary progress is nevertheless generally accepted in the field of Artificial Life. In this article we adopt the definition proposed by Shanahan (2012) to study the existence of evolutionary progress in an evolutionary simulation which we call HetCA. HetCA is a he...

Typically, Genetic Programming (GP) attempts to solve a problem by evolving solutions over a large, and usually pre-determined number of generations. However, overwhelming evidence shows that not only does the rate of performance improvement drop considerably after a few early generations, but that further improvement also comes at a considerable c...

We present an automated, end-to-end approach for Stage~1 breast cancer detection. The first phase of our proposed work-flow takes individual digital mammograms as input and outputs several smaller sub-images from which the background has been removed. Next, we extract a set of features which capture textural information from the segmented images. I...

This work introduces Wave, a divide and conquer approach to GP whereby a sequence of short, and dependent but potentially heterogeneous GP runs provides a collective solution ; the sequence akins a wave such that each short GP run is a period of the wave. Heterogeneity across periods results from varying settings of system parameters, such as popul...

This chapter describes a general approach for image classification using Genetic Programming (GP) and demonstrates this approach through the application of GP to the task of stage 1 cancer detection in digital mammograms. We detail an automated work-flow that begins with image processing and culminates in the evolution of classification models whic...

In this article we explore and develop a holistic scheme of self adaptive, individualized genetic operators combined with an adaptive tournament size together with a novel implementation of an inversion genetic operator which is suitable for tree based Genetic Programming. We test this scheme on several benchmark Binary Classification problems and...

In this paper we investigate a novel technique that optimizes the execution time of Grammatical Evolution through the usage of on-chip multiple processors. This technique, Multicore Grammatical Evolution (MCGE) evolves natively parallel programs with the help of OpenMP primitives through the grammars, such that not only can we exploit parallelism w...

For some time, Genetic Programming research has lagged behind the wider Machine Learning community in the study of generalisation, where the decomposition of generalisation error into bias and variance components is well understood. However, recent Genetic Programming contributions focusing on complexity, size and bloat as they relate to over-fitti...

There have been many studies undertaken to determine the efficacy of parameters and algorithmic components of Genetic Programming, but historically, generalization considerations have not been of central importance in such investigations. Recent contributions have stressed the importance of generalisation to the future development of the field. In...

We describe a fully automated workflow for performing stage 1 breast cancer detection with GP as its cornerstone. Mammograms are by far the most widely used method for detecting breast cancer in women, and its use in national screening can have a dramatic impact on early detection and survival rates. With the increased availability of digital mammo...

Typically, the quality of a solution in Genetic Programming (GP) is represented by a score on a given training sample. However, in Machine Learning, we are most interested in estimating the quality of the evolving individuals on unseen data. In this paper, we propose to simulate the effect of unseen data to direct training without actually using ad...

In Machine Learning classification tasks, the class imbalance problem is an important one which has received a lot of attention in the last few years. In binary classification, class imbalance occurs when there are significantly fewer examples of one class than the other. A variety of strategies have been applied to the problem with varying degrees...

This paper investigates a new application of a validation set when using a three data set methodology with Genetic Programming (GP). Our system uses Validation Pressure combined with Validation Elitism to influence fitness evaluation and population structure with the aim of improving the system’s ability to evolve individuals with an enhanced capac...

This paper investigates the leveraging of a validation data set with Genetic Programming (GP) to counteract over-fitting. It considers fitness on both training and validation fitness, combined with with an early stopping mechanism to improve generalisation while significantly reducing run times. The method is tested on six benchmark binary classifi...