Now showing 1 - 10 of 37
  • Publication
    Semantic-based subtree crossover applied to dynamic problems
    Although many real world problems are dynamic in nature, the study of Genetic Programming in dynamic environments is still immature. This paper investigates the application of some recently proposed semantic-based crossover operators on a series of dynamic problems. The operators studied include Semantic Similarity based Crossover and the Most Semantic Similarity based Crossover. The experimental results show the advantage of using semantic based crossovers when tackling dynamic problems.
    Scopus© Citations 6  612
  • Publication
    Tackling overfitting in evolutionary-driven financial model induction
    This chapter explores the issue of overfitting in grammar-based Genetic Programming. Tools such as Genetic Programming are well suited to problems in finance where we seek to learn or induce a model from data. Models that overfit the data upon which they are trained prevent model generalisation, which is an important goal of learning algorithms. Early stopping is a technique that is frequently used to counteract overfitting, but this technique often fails to identify the optimal point at which to stop training. In this chapter, we implement four classes of stopping criteria, which attempt to stop training when the generalisation of the evolved model is maximised. We show promising results using, in particular, one novel class of criteria, which measured the correlation between the training and validation fitness at each generation. These criteria determined whether or not to stop training depending on the measurement of this correlation - they had a high probability of being the best among a suite of potential criteria to be used during a run. This meant that they often found the lowest validation set error for the entire run faster than other criteria.
    Scopus© Citations 6  579
  • 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.
    Scopus© Citations 11  414
  • Publication
    Genetic Programming for the Induction of Seasonal Forecasts: A Study on Weather-derivatives
    The last ten years has seen the introduction and rapid growth of a market in weather derivatives, financial instruments whose payoffs are determined by the outcome of an underlying weather metric. These instruments allow organisations to protect themselves against the commercial risks posed by weather fluctuations and also provide investment opportunities for financial traders. The size of the market for weather derivatives is substantial, with a survey suggesting that the market size exceeded $45.2 Billion in 2005/2006 with most contracts being written on temperature-based metrics. A key problem faced by buyers and sellers of weather derivatives is the determination of an appropriate pricing model (and resulting price) for the financial instrument. A critical input into the pricing model is an accurate forecast of the underlying weather metric. In this study we induce seasonal forecasting temperature models by means of a Machine Learning algorithm. Genetic Programming (GP) is applied to learn an accurate, localised, long-term forecast of a temperature profile as part of the broader process of determining appropriate pricing model for weather-derivatives. Two different approaches for GP-based time-series modelling are adopted. The first is based on a simple system identification approach whereby the temporal index of the time-series is used as the sole regressor of the evolved model. The second is based on iterated single-step prediction that resembles autoregressive and moving average models in statistical time-series modelling. The major issue of effective model generalisation is tackled though the use of an ensemble learning technique that allows a family of forecasting models to be evolved using different training sets, so that predictions are formed by averaging the diverse model outputs. Empirical results suggest that GP is able to successfully induce seasonal forecasting models, and that search-based autoregressive models compose a more stable unit of evolution in terms of generalisation performance for the three datasets considered. In addition, the use of ensemble learning of 5-model predictors enhanced the generalisation ability of the system as opposed to single-model prediction systems. On a more general note, there is an increasing recognition of the utility of evolutionary methodologies for the modelling of meteorological, climatic and ecological phenomena, and this work also contributes to this literature.
    Scopus© Citations 12  3221
  • 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
    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.
    Scopus© Citations 33  710
  • Publication
    Evolving behaviour trees for the Mario AI competition using grammatical evolution
    This paper investigates the applicability of Genetic Programming type systems to dynamic game environments. Grammatical Evolution was used to evolve Behaviour Trees, in order to create controllers for the Mario AI Benchmark. The results obtained reinforce the applicability of evolutionary programming systems to the development of artificial intelligence in games, and in dynamic systems in general, illustrating their viability as an alternative to more standard AI techniques.
      1521Scopus© Citations 59
  • Publication
    Objective function design in a grammatical evolutionary trading system
    Designing a suitable objective function is an essential step in successfully applying an evolutionary algorithm to a problem. In this study we apply a grammar-based Genetic Programming algorithm called Grammatical Evolution to the problem of trading model induction and carry out a number of experiments to assess the effect of objective function design on the trading characteristics of the evolved strategies. The paper concludes with in and out-of-sample results, and indicates a number of avenues of future work.
    Scopus© Citations 5  1785
  • Publication
    Semantically-based crossover in genetic programming : application to real-valued symbolic regression
    We investigate the effects of semantically-based crossover operators in Genetic Programming, applied to real-valued symbolic regression problems. We propose two new relations derived from the semantic distance between subtrees, known as Semantic Equivalence and Semantic Similarity. These relations are used to guide variants of the crossover operator, resulting in two new crossover operators – Semantics Aware Crossover (SAC) and Semantic Similarity-based Crossover (SSC). SAC, was introduced and previously studied, is added here for the purpose of comparison and analysis. SSC extends SAC by more closely controlling the semantic distance between subtrees to which crossover may be applied. The new operators were tested on some real-valued symbolic regression problems and compared with Standard Crossover (SC), Context Aware Crossover (CAC), Soft Brood Selection (SBS), and No Same Mate (NSM) selection. The experimental results show on the problems examined that, with computational effort measured by the number of function node evaluations, only SSC and SBS were significantly better than SC, and SSC was often better than SBS. Further experiments were also conducted to analyse the perfomance sensitivity to the parameter settings for SSC. This analysis leads to a conclusion that SSC is more constructive and has higher locality than SAC, NSM and SC; we believe these are the main reasons for the improved performance of SSC.
    Scopus© Citations 204  1722
  • 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