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Nicolau, Miguel
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Nicolau, Miguel
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Nicolau, Miguel
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Now showing 1 - 10 of 42
- PublicationA GAuGE Approach to Learning DFA from Noisy Samples(2004-06-30)
; ; This paper describes the adaptation of the GAuGE system to classify binary sequences generated by random DFA. Experiments were conducted, which, although not highly successful, illustrate the potential of applying GAuGE like systems to this problem domain.104 - PublicationDesigning a Massive Dataset Framework for the Grammatical Evolution System(2016-07-06)In this study, a GE-framework is built, in an effort to apply it to huge datasets. Combining statistical techniques such as appropriate error measures and data splitting, population-based improvements such as mass parallelisation, and even specific techniques such as grammar design and repeat management, GE is applied for the first time to massive datasets, such as the Higgs dataset (eleven million samples).
97 - PublicationCrossover, Population Dynamics and Convergence in the GAuGE SystemThis paper presents a study of the effectiveness of a recently presented crossover operator for the GAuGE system. This crossover, unlike the traditional crossover employed previously, preserves the association of positions and values which exists in GAuGE genotype strings, and as such is more adequate for problems where the meaning of an allele is dependent on its placement in the phenotype string. Results obtained show that the new operator improves the performance of the GAuGE system on simple binary problems, both when position-sensitive data is manipulated and not.
360Scopus© Citations 1 - PublicationValidation of a morphogenesis Model of Drosophila Early Development by a Multi-objective evolutionary Optimization AlgorithmWe apply evolutionary computation to calibrate the parameters of a morphogenesis model of Drosophila early development. The model aims to describe the establishment of the steady gradients of Bicoid and Caudal proteins along the antero-posterior axis of the embryo of Drosophila. The model equations consist of a system of non-linear parabolic partial differential equations with initial and zero flux boundary conditions. We compare the results of single- and multi-objective variants of the CMA-ES algorithm for the model the calibration with the experimental data. Whereas the multiobjective algorithm computes a full approximation of the Pareto front, repeated runs of the single-objective algorithm give solutions that dominate (in the Pareto sense) the results of the multi-objective approach. We retain as best solutions those found by the latter technique. From the biological point of view, all such solutions are all equally acceptable, and for our test cases, the relative error between the experimental data and validated model solutions on the Pareto front are in the range 3% − 6%. This technique is general and can be used as a generic tool for parameter calibration problems.
357Scopus© Citations 9 - PublicationHow Functional Dependency Adapts to Salience Hierarchy in the GAuGE SystemGAuGE is a position independent genetic algorithm that suffers from neither under nor over-specification, and uses a genotype to phenotype mapping process. By specifying both the position and the value of each gene, it has the potential to group important data together in the genotype string, to prevent it from being broken up and disrupted during the evolution process. To test this ability, GAuGE was applied to a set of problems with exponentially scaled salience. The results obtained demonstrate that GAuGE is indeed moving the more salient genes to the start of the genotype strings, creating robust individuals that are built in a progressive fashion from the left to the right side of the genotype.
405Scopus© Citations 6 - PublicationApplying Genetic Regulatory Networks to Index TradingThis paper explores the computational power of genetic regulatory network models, and the practicalities of applying these to real-world problems. The specific domain of financial trading is tackled; this is a problem where time-dependent decisions are critical, and as such benefits from the differential gene expression that these networks provide. The results obtained are on par with the best found in the literature, and highlight the applicability of these models to this type of problem.
447Scopus© Citations 6 - PublicationOn the Evolution of Scale-Free Topologies with a Gene Regulatory Network ModelA novel approach to generating scale-free network topologies is introduced, based on an existing artificial gene regulatory network model. From this model, different interaction networks can be extracted, based on an activation threshold. By using an evolutionary computation approach, the model is allowed to evolve, in order to reach specific network statistical measures. The results obtained show that, when the model uses a duplication and divergence initialisation, such as seen in nature, the resulting regulation networks not only are closer in topology to scale-free networks, but also require only a few evolutionary cycles to achieve a satisfactory error value.
318Scopus© Citations 18 - PublicationAutomatic Grammar Complexity Reduction in Grammatical Evolution(2004-06-30)Grammatical Evolution is an automatic programming system, where a population of binary strings is evolved, from which phenotype strings are generated through a mapping process, that employs a grammar to define the syntax of such output strings. This paper presents a study of the effect of grammar size and complexity on the performance of the system. A simple method to reduce the number of non-terminal symbols in a grammar is presented, along with the reasoning behind it. Results obtained on a series of problems suggest that performance can be increased with the approach presented.
206 - PublicationIntroducing Grammar Based Extensions for Grammatical EvolutionThis paper presents a series of extensions to standard Grammatical Evolution. These grammar-based extensions facilitate the exchange of knowledge between genotype and phenotype strings, thus establishing a better correlation between the search and solution spaces, typically separated in Grammatical Evolution. The results obtained illustrate the practical advantages of these extensions, both in terms of convenience and potential increase in performance.
581 - PublicationExploring grammatical modification with modules in grammatical evolutionThere have been many approaches to modularity in the field of evolutionary computation, each tailored to function with a particular representation. This research examines one approach to modularity and grammar modification with a grammar-based approach to genetic programming, grammatical evolution (GE). Here, GE’s grammar was modified over the course of an evolutionary run with modules in order to facilitate their appearance in the population. This is the first step in what will be a series of analysis on methods of modifying GE’s grammar to enhance evolutionary performance. The results show that identifying modules and using them to modify GE’s grammar can have a negative effect on search performance when done improperly. But, if undertaken thoughtfully, there are possible benefits to dynamically enhancing the grammar with modules identified during evolution.
461Scopus© Citations 10