Now showing 1 - 10 of 10
  • 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.
    Scopus© Citations 1  4901
  • Publication
    Natural computing in finance : a review
    The field of Natural Computing (NC) has advanced rapidly over the past decade. One significant offshoot of this progress has been the application of NC methods in finance. This chapter provides an introduction to a wide range of financial problems to which NC methods have been usefully applied. The chapter also identifies open issues and suggests future directions for the application of NC methods in finance.
      2899Scopus© Citations 6
  • Publication
    Applying Genetic Regulatory Networks to Index Trading
    This 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.
      444Scopus© Citations 6
  • Publication
    Higher-order functions in aesthetic EC encodings
    The use of higher-order functions, as a method of abstraction and re-use in EC encodings, has been the subject of relatively little research. In this paper we introduce and give motivation for the ideas of higher-order functions, and describe their general advantages in EC encodings. We implement grammars using higher-order ideas for two problem domains, music and 3D architectural design, and use these grammars in the grammatical evolution paradigm. We demonstrate four advantages of higher-order functions (patterning of phenotypes, non-entropic mutations, compression of genotypes, and natural expression of artistic knowledge) which lead to beneficial results on our problems.
      827Scopus© Citations 13
  • Publication
    Implementing an intuitive mutation operator for interactive evolutionary 3D design
    Locality - how well neighbouring genotypes correspond to neighbouring phenotypes - has been described as a key element in Evolutionary Computation. Grammatical Evolution (GE) is a generative system as it uses grammar rules to derive a program from an integer encoded genome. The genome, upon which the evolutionary process is carried out, goes through several transformations before it produces an output. The aim of this paper is to investigate the impact of locality during the generative process using both qualitative and quantitative techniques. To explore this, we examine the effects of standard GE mutation using distance metrics and conduct a survey of the output designs. There are two different kinds of event that occur during standard GE Mutation. We investigate how each event type affects the locality on different phenotypic stages when applied to the problem of interactive design generation.
    Scopus© Citations 9  612
  • Publication
    Improving the generalisation ability of genetic programming with semantic similarity based crossover
    This paper examines the impact of semantic control on the ability of Genetic Programming (GP) to generalise via a semantic based crossover operator (Semantic Similarity based Crossover - SSC). The use of validation sets is also investigated for both standard crossover and SSC. All GP systems are tested on a number of real-valued symbolic regression problems. The experimental results show that while using validation sets barely improve generalisation ability of GP, by using semantics, the performance of Genetic Programming is enhanced both on training and testing data. Further recorded statistics shows that the size of the evolved solutions by using SSC are often smaller than ones obtained from GP systems that do not use semantics. This can be seen as one of the reasons for the success of SSC in improving the generalisation ability of GP.
    Scopus© Citations 33  794
  • Publication
    An analysis of genotype-phenotype maps in grammatical evolution
    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.
      624Scopus© Citations 29
  • Publication
    Interactive interpolating crossover in grammatical evolution
    Interactive interpolating crossover allows a user to quickly see a large number of individuals formed by interactively-controlled interpolation between two or more parents. We study it here for the first time in the context of grammatical evolution (GE). We define methods of quantifying the behaviour of interpolations and use them to compare two methods of performing interpolation and two encodings for GE, one standard and one new. We conclude that a Cartesian interpolation combined with a novel developmental-style GE encoding gives the most usable results. We make connections between our work and broader issues of genotype-phenotype mappings, landscapes, and operators.
      504Scopus© Citations 1
  • Publication
    Evolving a Ms. PacMan controller using grammatical evolution
    In this paper we propose an evolutionary approach capable of successfully combining rules to play the popular video game, Ms. Pac- Man. In particular we focus our attention on the benefits of using Gram- matical Evolution to combine rules in the form of “if then perform ”. We defined a set of high-level functions that we think are necessary to successufully maneuver Ms. Pac-Man through a maze while trying to get the highest possible score. For comparison purposes, we used four Ms. Pac-Man agents, including a hand-coded agent, and tested them against three different ghosts teams. Our approach shows that the evolved controller achieved the highest score among all the other tested controllers, regardless of the ghost team used.
    Scopus© Citations 22  807
  • 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.
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