Now showing 1 - 10 of 71
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Dynamic environments can speed up evolution with genetic programming

2011, O'Neill, Michael, Nicolau, Miguel, Brabazon, Anthony

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.

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A Hybrid Algorithm for Multi-objective Test Case Selection

2018-10-04, Saber, Takfarinas, Delavernhe, Florian, Papadakis, Mike, O'Neill, Michael, Ventresque, Anthony

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.

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A preliminary investigation of overfitting in evolutionary driven model induction : implications for financial modelling

2011-04-27, Tuite, Clíodhna, Agapitos, Alexandros, O'Neill, Michael, Brabazon, Anthony

This paper investigates the effects of early stopping as a method to counteract overfitting in evolutionary data modelling using Genetic Programming. Early stopping has been proposed as a method to avoid model overtraining, which has been shown to lead to a significant degradation of out-of-sample performance. If we assume some sort of performance metric maximisation, the most widely used early training stopping criterion is the moment within the learning process that an unbiased estimate of the performance of the model begins to decrease after a strictly monotonic increase through the earlier learning iterations. We are conducting an initial investigation on the effects of early stopping in the performance of Genetic Programming in symbolic regression and financial modelling. Empirical results suggest that early stopping using the above criterion increases the extrapolation abilities of symbolic regression models, but is by no means the optimal training-stopping criterion in the case of a real-world financial dataset.

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Evolving trading rule-based policies

2010-04, Bradley, Robert, Brabazon, Anthony, O'Neill, Michael

Trading-rule representation is an important factor to consider when designing a quantitative trading system. This study implements a trading strategy as a rule-based policy. The result is an intuitive human-readable format which allows for seamless integration of domain knowledge. The components of a policy are specified and represented as a set of rewrite rules in a context-free grammar. These rewrite rules define how the components can be legally assembled. Thus, strategies derived from the grammar are well-formed, domain-specific, solutions. A grammar-based Evolutionary Algorithm, Grammatical Evolution (GE), is then employed to automatically evolve intra-day trading strategies for the U.S. Stock Market. The GE methodology managed to discover profitable rules with realistic transaction costs included. The paper concludes with a number of suggestions for future work.

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Tonality driven piano compositions with Grammatical Evolution

2015-05-28, Loughran, Róisín, McDermott, James, O'Neill, Michael

We present a novel method of creating piano melodies with Grammatical Evolution (GE). The system employs a context free grammar in combination with a tonality-driven fitness function to create a population of piano melodies. The grammar is designed to create a variety of styles of musical events within each melody such as runs, arpeggios, turns and chords without any a priori musical information in regards to key or time signature. The fitness of the individuals is calculated as a measure of their tonality defined by a statistical distribution of the pitches in each piece. A number of short compositions are presented demonstrating that our system is capable of creating music that is interesting and unpredictable.

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Neutrality in evolutionary algorithms... what do we know?

2011-03-02, Galván-López, Edgar, Poli, Riccardo, Kattan, Ahmed, O'Neill, Michael, Brabazon, Anthony

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.

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A comparison of GE and TAGE in dynamic environments

2011-07-12, Murphy, Eoin, O'Neill, Michael, Brabazon, Anthony

The lack of study of genetic programming in dynamic environments is recognised as a known issue in the field of genetic programming. This study compares the performance of two forms of genetic programming, grammatical evolution and a variation of grammatical evolution which uses tree-adjunct grammars, on a series of dynamic problems. Mean best fitness plots for the two representations are analysed and compared.

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Comparing the performance of the evolvable πgrammatical evolution genotype-phenotype pap to grammatical evolution in the dynamic Ms. Pac-Man environment

2010-07, Galván-López, Edgar, Fagan, David, Murphy, Eoin, Swafford, John Mark, Agapitos, Alexandros, O'Neill, Michael, Brabazon, Anthony

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.

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Tracer spectrum : a visualisation method for distributed evolutionary computation

2011-09, O'Neill, Michael, Brabazon, Anthony, Hemberg, Erik

We present a novel visualisation method for island-based evolutionary algorithms based on the concept of tracers as adopted in medicine and molecular biology to follow a biochemical process. For example, a radioisotope or dye can be used to replace a stable component of a biological compound, and the signal from the radioisotope can be monitored as it passes through the body to measure the compound’s distribution and elimination from the system. In a similar fashion we attach a tracer dye to individuals in each island, where each individual in any one island is marked with the same colour, and each island then has its own unique colour signal. We can then monitor how individuals undergoing migration events are distributed throughout the entire island ecosystem, thereby allowing the user to visually monitor takeover times and the resulting loss of diversity. This is achieved by visualising each island as a spectrum of the tracer dye associated with each individual. Experiments adopting different rates of migration and network connectivity confirm earlier research which predicts that island models are extremely sensitive to the size and frequency of migrations

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Maximum margin decision surfaces for increased generalisation in evolutionary decision tree learning

2011-04-27, Agapitos, Alexandros, O'Neill, Michael, Brabazon, Anthony, Theodoridis, Theodoros

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.