Now showing 1 - 10 of 50
  • 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
    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
    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
    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
    Dynamic trade execution : a grammatical evolution approach
    (Inderscience Enterprises, 2011-02) ; ;
    Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument. Investors wishing to execute large orders face a tradeoff between market impact and opportunity cost. Trade execution strategies are designed to balance out these costs, thereby minimising total trading cost. Despite the importance of optimising the trade execution process, this is difficult to do in practice due to the dynamic nature of markets and due to our imperfect understanding of them. In this paper, we adopt a novel approach, combining an evolutionary methodology whereby we evolve high-quality trade execution strategies, with an agent-based artificial stock market, wherein the evolved strategies are tested. The evolved strategies are found to outperform a series of benchmark strategies and several avenues are suggested for future work.
  • Publication
    An efficient customer search tool within an anti-money laundering application implemented on an internaitonal bank's dataset
    Today, money laundering (ML) poses a serious threat not only to financial institutions but also to the nations. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliché of drug trafficking to financing terrorism and surely not forgetting personal gain. Most of the financial institutions internationally have been implementing anti-money laundering solutions (AML) to fight investment fraud activities. In AML, the customer identification is an important task which helps AML experts to monitor customer habits: some being customer domicile, transactions that they are involved in etc. However, simple query tools provided by current DBMS as well as naive approaches in customer searching may produce incorrect and ambiguous results and their processing time is also very high due to the complexity of the database system architecture. In this paper, we present a new approach for identifying customers registered in an investment bank. This approach is developed as a tool that allows AML experts to quickly identify customers who are managed independently across separate databases. It is tested on real-world datasets, which are real and large financial datasets. Some preliminary experimental results show that this new approach is efficient and effective.
  • Publication
    GEVA : grammatical evolution in Java
    We are delighted to announce the release of GEVA an open source software implementation of Grammatical Evolution (GE) in Java. Grammatical Evolution in Java (GEVA) was developed at UCD’s Natural Computing Research & Applications group (
  • Publication
    Recent patents on genetic programming
    (Bentham Science Publishers, 2009-01) ;
    Genetic Programming is a form of Natural Computing which adopts principles from neo-Darwinian evolution to automatically solve problems. It is a model induction method in that both the structure and parameters of the solution are explored simultaneously. Genetic Programming is a particularly interesting method as it is claimed to be an invention machine, producing solutions to problems that are competitive and in some cases superior to those produced by human experts. Its best solutions have become patentable inventions in their own right. In this article, we overview some of the recent patents relating to Genetic Programming over the past three years. In light of the number and diversity of patent applications during this period, it is clear that Genetic Programming is a vibrant field of research, which is having a significant impact on real-world applications, and is demonstrating clear commercial potential.
  • Publication
    An Agent-based Modeling Approach to Study Price Impact
    (IEEE Press, 2012-03-29) ;
    Price impact models are important for devising trade execution strategies. However, a proper characterization of price impacts is still lacking. This study models the price impact using an agent-based modeling approach. The purpose of this paper is to investigate whether agent intelligence is a necessary condition when seeking to construct realistic price impact with an artificial market simulation. We build a zero- intelligence based artificial limit order market model. Our model distinguishes limit orders according to their order aggressiveness and takes into account some observed facts including log-normal distributed order sizes and power-law distributed limit order placements. The model is calibrated using trades and orders data from the London Stock Exchange. The results indicate that agent intelligence is needed when simulating an artificial market where replicating price impact is a concern.
      1379Scopus© Citations 14
  • Publication
    Maximum margin decision surfaces for increased generalisation in evolutionary decision tree learning
    Decision tree learning is one of the most widely used and practical methods for inductive inference. We present a novel method that increases the generalisation of genetically-induced classification trees, which employ linear discriminants as the partitioning function at each internal node. Genetic Programming is employed to search the space of oblique decision trees. At the end of the evolutionary run, a (1+1) Evolution Strategy is used to geometrically optimise the boundaries in the decision space, which are represented by the linear discriminant functions. The evolutionary optimisation concerns maximising the decision-surface margin that is defined to be the smallest distance between the decision-surface and any of the samples. Initial empirical results of the application of our method to a series of datasets from the UCI repository suggest that model generalisation benefits from the margin maximisation, and that the new method is a very competent approach to pattern classification as compared to other learning algorithms.
      342Scopus© Citations 11