Now showing 1 - 10 of 71
  • Publication
    The syntax of stock selection : grammatical evolution of a stock picking model
    A significant problem in the area of stock selection is that of identifying the factors that affect a security’s return. While modern portfolio theory suggests a linear multi-factor model in the form of Arbitrage Pricing Theory it does not suggest the identity, or even the number, of risk factors in the model. Candidate factors for inclusion in a fundamental model can include hundreds of data points for each firm and with thousands of firms in the fund manager’s selection universe the model specification problem encompasses a large, computationally intense search space. Grammatical Evolution (GE) is a form of evolutionary computing that has been used successfully in model induction problems involving large search spaces. GE is applied to evolve a stock selection model with a customized mapping process developed specifically to enhance the performance of evolutionary operators for this problem. Stock selection models are rated using fitness functions commonly employed in asset management; the information coefficient and the inter-quantile return spread. The findings of the paper indicate that evolutionary computing is an excellent tool for the development of stock picking models.
      2943Scopus© Citations 2
  • 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.
      211
  • Publication
    Comparing the performance of the evolvable πgrammatical evolution genotype-phenotype pap to grammatical evolution in the dynamic Ms. Pac-Man environment
    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.
      582Scopus© Citations 11
  • 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.
      398Scopus© Citations 11
  • Publication
    Neutrality in evolutionary algorithms... what do we know?
    Over the last years, the effects of neutrality have attracted the attention of many researchers in the Evolutionary Algorithms (EAs) community. A mutation from one gene to another is considered as neutral if this modification does not affect the phenotype. This article provides a general overview on the work carried out on neutrality in EAs. Using as a framework the origin of neutrality and its study in different paradigms of EAs (e.g., Genetic Algorithms, Genetic Programming), we discuss the most significant works and findings on this topic. This work points towards open issues, which the community needs to address.
      617Scopus© Citations 42
  • Publication
    Investigating mapping order in πGE
    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.
      519Scopus© Citations 3
  • 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.
      458Scopus© Citations 6
  • 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.
      4917Scopus© Citations 1
  • Publication
    Introducing Semantic-Clustering Selection in Grammatical Evolution
    Semantics 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.
      422Scopus© Citations 5
  • Publication
    Examining mutation landscapes in grammar based genetic programming
    Representation is a very important component of any evolutionary algorithm. Changing the representation can cause an algorithm to perform very differently. Such a change can have an effect that is difficult to understand. This paper examines what happens to the grammatical evolution algorithm when replacing the commonly used context-free grammar representation with a tree-adjunct grammar representation. We model the landscapes produced when using integer flip mutation with both representations and compare these landscapes using visualisation methods little used in the field of genetic programming.
      674Scopus© Citations 9