Now showing 1 - 10 of 71
  • Publication
    Dynamic environments can speed up evolution with genetic programming
    (University College Dublin. School of Computer Science and Informatics, 2011) ; ;
    We present a study of dynamic environments with genetic programming to ascertain if a dynamic environment can speed up evolution when compared to an equivalent static environment. We present an analysis of the types of dynamic variation which can occur with a variable-length representation such as adopted in genetic programming identifying modular varying, structural varying and incremental varying goals. An empirical investigation comparing these three types of varying goals on dynamic symbolic regression benchmarks reveals an advantage for goals which vary in terms of increasing structural complexity. This provides evidence to support the added difficulty variable length representations incur due to their requirement to search structural and parametric space concurrently, and how directing search through varying structural goals with increasing complexity can speed up search with genetic programming.
  • Publication
    A Hybrid Algorithm for Multi-objective Test Case Selection
    Testing is crucial to ensure the quality of software systems – but testing is an expensive process, so test managers try to minimise the set of tests to run to save computing resources and speed up the testing process and analysis. One problem is that there are different perspectives on what is a good test and it is usually not possible to compare these dimensions. This is a perfect example of a multi-objective optimisation problem, which is hard — especially given the scale of the search space here. In this paper, we propose a novel hybrid algorithm to address this problem. Our method is composed of three steps: a greedy algorithm to find quickly some good solutions, a genetic algorithm to increase the search space covered and a local search algorithm to refine the solutions. We demonstrate through a large scale empirical evaluation that our method is more reliable (better whatever the time budget) and more robust (better whatever the number of dimensions considered) – in the scenario with 4 objectives and a default execution time, we are 178% better in hypervolume on average than the state-of-the-art algorithms.
  • Publication
    Interactive operators for evolutionary architectural design
    (University College Dublin. School of Computer Science and Informatics, 2011-04-12) ; ;
    In this paper we explore different techniques that allow the user to direct interactive evolutionary search. Broadening interaction beyond simple evaluation increases the amount of feedback and bias a user can apply to the search. Increased feedback will have the effect of directing the algorithm to more fruitful areas of the search space. This paper examines whether additional feedback from the user can be a benefit to the problem of evolutionary design. We find that the interface between the user and the search space plays a vital role in this process.
  • Publication
    Dynamic 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.
  • Publication
    Wave height quantification using land based seismic data with grammatical evolution
    Accurate, real time, continuous ocean wave height measurements are required for the initialisation of ocean wave forecast models, model hindcasting, and climate studies. These measurements are usually obtained using in situ ocean buoys or by satellite altimetry, but are sometimes incomplete due to instrument failure or routine network upgrades. In such situations, a reliable gap filling technique is desirable to provide a continuous and accurate ocean wave field record. Recorded on a land based seismic network are continuous seismic signals known as microseisms. These microseisms are generated by the interactions of ocean waves and will be used in the estimation of ocean wave heights. Grammatical Evolution is applied in this study to generate symbolic models that best estimate ocean wave height from terrestrial seismic data, and the best model is validated against an Artificial Neural Network. Both models are tested over a five month period of 2013, and an analysis of the results obtained indicates that the approach is robust and that it is possible to estimate ocean wave heights from land based seismic data.
  • Publication
    GEVA - Grammatical Evolution in Java (v1.0)
    (University College Dublin. School of Computer Science and Informatics, 2008-12-05) ; ; ; ; ;
    GEVA is an open source implementation of Grammatical Evolution in Java developed at UCD’s Natural Computing Research & Applications group. As well as providing the characteristic genotype-phenotype mapper of GE a search algorithm engine and a simple GUI are also provided. A number of sample problems and tutorials on how to use and adapt GEVA have been developed.
  • Publication
    Evolutionary design using grammatical evolution and shape grammars : designing a shelter
    A new evolutionary design tool is presented, which uses shape grammars and a grammar-based form of evolutionary computation, grammatical evolution (GE). Shape grammars allow the user to specify possible forms, and GE allows forms to be iteratively selected, recombined and mutated: this is shown to be a powerful combination of techniques. The potential of GE and shape grammars for evolutionary design is examined by attempting to design a single-person shelter to be evaluated by collaborators from the University College Dublin School of Architecture, Landscape, and Engineering. The team was able to successfully generate conceptual shelter designs based on scrutiny from the collaborators. A number of avenues for future work are highlighted arising from the case study.
  • Publication
    Genotype representations in grammatical evolution
    Grammatical evolution (GE) is a form of grammar-based genetic programming. A particular feature of GE is that it adopts a distinction between the genotype and phenotype similar to that which exists in nature by using a grammar to map between the genotype and phenotype. Two variants of genotype representation are found in the literature, namely, binary and integer forms. For the first time we anal- yse and compare these two representations to determine if one has a performance advantage over the other. As such this study seeks to extend our understanding of GE by examining the impact of different genotypic representations in order to determine whether certain representations, and associated diversity-generation op- erators, improve GE’s efficiency and effectiveness. Four mutation operators using two different representations, binary and gray code representation respectively, are investigated. The differing combinations of representation and mutation operator are tested on three benchmark problems. The results provide support for the use of an integer-based genotypic representation as the alternative representations do not exhibit better performance, and the integer reprensentation provides a statistically significant advantage on one of the three benchmarks. In addition, a novel wrapping operator for the binary and gray code representations is examined, and it is found that across the three problems examined there is no general trend to recommend the adoption of an alternative wrapping operator. The results also back up earlier findings which support the adoption of wrapping.
      603Scopus© Citations 32
  • Publication
    Evolutionary computation and trade execution
    Although there is a plentiful literature on the use of evolutionary methodologies for the trading of Financial assets, little attention has been paid to the issue of efficient trade execution. Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of interest. This chapter introduces the concept of trade execution and outlines the limited prior work applying evolutionary computing methods for this task. Furthermore, we build an Agent-based Artificial Stock Market and apply a Genetic Algorithm to evolve an efficient trade execution strategy. Finally, we suggest a number of opportunities for future research.
      4813Scopus© Citations 1