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Fagan, David
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Fagan, David
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Fagan, David
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Now showing 1 - 8 of 8
- PublicationDynamic ant : introducing a new benchmark for genetic programming in dynamic environments(University College Dublin. School of Computer Science and Informatics, 2011-04-14)
; ; ; ; In this paper we present a new variant of the ant problem in the dynamic problem domain. This approach presents a functional dynamism to the problem landscape, where by the behaviour of the ant is driven by its ability to explore the search space being constrained. This restriction is designed in such a way as to ensure that no generalised solution to the problem is possible, thus providing a functional change in behaviour.269 - PublicationDeep Evolution of Feature Representations for Handwritten Digit Recognition(IEEE, 2015-05-28)
; ; ; ; ; A training protocol for learning deep neural networks, called greedy layer-wise training, is applied to the evolution of a hierarchical, feed-forward Genetic Programming based system for feature construction and object recognition. Results on a popular handwritten digit recognition benchmark clearly demonstrate that two layers of feature transformations improves generalisation compared to a single layer. In addition, we show that the proposed system outperforms several standard Genetic Programming systems, which are based on hand-designed features, and use different program representations and fitness functions.315Scopus© Citations 11 - PublicationIntroducing Semantic-Clustering Selection in Grammatical EvolutionSemantics has gained much attention in the last few years and new advanced crossover and mutation operations have been created which use semantic information to improve the quality and generalisability of individuals in genetic programming. In this paper we present a new selection operator in grammatical evolution which uses semantic information of individuals instead of just the fitness value. The semantic traits of an individual are stored in a vector. An unsupervised learning technique is used to cluster individuals based on their semantic vector. Individuals are only allowed to reproduce with individuals from the same cluster to preserve semantic locality and intensify the search in a certain semantic area. At the same time, multiple semantic areas are covered by the search as there exist multiple clusters which cover different areas and therefore preserve semantic diversity. This new selection operator is tested on several symbolic regression benchmark problems and compared to grammatical evolution with tournament selection to analyse its performance.
414Scopus© Citations 5 - PublicationAn analysis of genotype-phenotype maps in grammatical evolution(Springer, 2010)
; ; ; ; We present an analysis of the genotype-phenotype map in Grammatical Evolution (GE). The standard map adopted in GE is a depth-first expansion of the non-terminal symbols during the derivation sequence. Earlier studies have indicated that allowing the path of the expansion to be under the guidance of evolution as opposed to a de- terministic process produced significant performance gains on all of the benchmark problems analysed. In this study we extend this analysis to in- clude a breadth-first and random map, investigate additional benchmark problems, and take into consideration the implications of recent results on alternative grammar representations with this new evidence. We con- clude that it is possible to improve the performance of grammar-based Genetic Programming by the manner in which a genotype-phenotype map is performed.626Scopus© Citations 29 - PublicationInvestigating mapping order in πGE(IEEE, 2010-07)
; ; ; ; ; We present an investigation into the genotype-phenotype map in Position Independent Grammatical Evolution (πGE). Previous studies have shown πGE to exhibit a performance increase over standard GE. The only difference between the two approaches is in how the genotype-phenotype mapping process is performed. GE uses a leftmost non terminal expansion, while πGE evolves the order of mapping as well as the content. In this study, we use the idea of focused search to examine which aspect of the πGE mapping process provides the lift in performance over standard GE by applying our approaches to four benchmark problems taken from specialised literature. We examined the traditional πGE approach and compared it to two setups which examined the extremes of mapping order search and content search, and against setups with varying ratios of content and order search. In all of these tests a purely content focused πGE was shown to exhibit a performance gain over the other setups.509Scopus© Citations 3 - PublicationGenotype-phenotype mapping in dynamic environments with grammatical evolutionThe application of a genotype-phenotype mapping in Evolutionary Computation is not a new idea, however, how this mapping process is interpreted, and implemented varies wildly. In the majority of cases a very simple abstraction of the biological genotype-phenotype mapping is used, but as our understanding of this process increases, the deficiencies in current approaches become more evident. In this paper, an outline of what approaches have been taken in the investigation of the genotype-phenotype map in Grammatical Evolution are presented and an outline of proposed future work is introduced.
385Scopus© Citations 2 - PublicationInvestigation of the performance of different mapping orders for GE on the max problem(Springer, 2011-04-27)
; ; ; ; ; We present an analysis of how the genotype-phenotype map in Grammatical Evolution (GE) can effect performance on the Max Problem. Earlier studies have demonstrated a performance decrease for Position independent Grammatical Evolution (πGE ) in this problem domain. In πGE the genotype-phenotype map is changed so that the evolutionary algorithm controls not only what the next expansion will be but also the choice of what position in the derivation tree is expanded next. In this study we extend previous work and investigate whether the ability to change the order of expansion is responsible for the performance decrease or if the problem is simply that a certain order of expansion in the genotype-phenotype map is responsible. We conclude that the reduction of performance in the Max problem domain by πGE is rooted in the way the genotype-phenotype map and the genetic operators used with this mapping interact.417Scopus© Citations 4 - PublicationComparing the performance of the evolvable πgrammatical evolution genotype-phenotype pap to grammatical evolution in the dynamic Ms. Pac-Man environment(IEEE, 2010-07)
; ; ; ; ; ; In this work, we examine the capabilities of two forms of mappings by means of Grammatical Evolution (GE) to successfully generate controllers by combining high-level functions in a dynamic environment. In this work we adopted the Ms. Pac-Man game as a benchmark test bed. We show that the standard GE mapping and Position Independent GE (πGE) mapping achieve similar performance in terms of maximising the score. We also show that the controllers produced by both approaches have an overall better performance in terms of maximising the score compared to a hand-coded agent. There are, however, significant differences in the controllers produced by these two approaches: standard GE produces more controllers with invalid code, whereas the opposite is seen with πGE.569Scopus© Citations 11