Michael Kirley

A fusion model of HMM, ANN and GA for stock market forecasting

In this paper we propose and implement a fusion model by combining the Hidden Markov Model (HMM), Artificial Neural Networks (ANN) and Genetic Algorithms (GA) to forecast financial market behaviour. The developed tool can be used for in depth analysis of the stock market. Using ANN, the daily stock prices are transformed to independent sets of values that become input to HMM. We draw on GA to optimize the initial parameters of HMM. The trained HMM is used to identify and locate similar patterns in the historical data. The price differences between the matched days and the respective next day are calculated. Finally, a weighted average of the price differences of similar patterns is obtained to prepare a forecast for the required next day. Forecasts are obtained for a number of securities in the IT sector and are compared with a conventional forecast method.

Multi-objective planning for workflow execution on Grids

Utility grids create an infrastructure for enabling users to consume services transparently over a global network. When optimizing workflow execution on utility grids, we need to consider multiple quality of service (QoS) parameters including service prices and execution time. These optimization objectives may be in conflict. In this paper, we have proposed a workflow execution planning approach using multi-objective evolutionary algorithms (MOEAs). Our goal was to generate a set of trade-off scheduling solutions according to the users QoS requirements. The alternative trade-off solutions offer more flexibility to users when estimating their QoS requirements of workflow executions. Simulation results show that MOEAs are able to find a range of compromise solutions in a short computational time.

Effects of Iterated Interactions in Multiplayer Spatial Evolutionary Games

Mechanisms promoting the evolution of cooperation in two players and two strategies (22) evolutionary games have been investigated in great detail over the past decades. Understanding the effects of repeated interactions in multiplayer spatial games, however, is a formidable challenge. In this paper, we present a multiplayer evolutionary game model in which agents play iterative games in spatial populations. -player versions of the well-known Prisoners Dilemma and the Snowdrift games are used as the basis of the investigation. These games were chosen as they have emerged as the most promising mathematical metaphors for studying cooperative phenomena. Here, we have adopted an experimental approach to study the emergent behavior, exploring different parameter configurations via numerical simulations. Key model parameters include the cost-to-benefit ratio, the size of groups, the number of repeated encounters, and the interaction topology. Our simulation results reveal that, while the introduction of iterated interactions does promote higher levels of cooperative behavior across a wide range of parameter settings, the cost-to-benefit ratio and group size are important factors in determining the appropriate length of beneficial repeated interactions. In particular, increasing the number of iterated interactions may have a detrimental effect when the cost-to-benefit ratio and group size are small.

On performance metrics and particle swarm methods for dynamic multiobjective optimization problems

This paper describes two performance measures for measuring an EMO (evolutionary multiobjective optimization) algorithms ability to track a time-varying Pareto-front in a dynamic environment. These measures are evaluated using a dynamic multiobjective test function and a dynamic multiobjective PSO, maximinPSOD, which is capable of handling dynamic multiobjective optimization problems. maximinPSOD is an extension from a previously proposed multiobjective PSO, maximinPSO. Our results suggest that these performance measures can be used to provide useful information about how well a dynamic EMO algorithm performs in tracking a time-varying Pareto-front. The results also show that maximinPSOD can be made self-adaptive, tracking effectively the dynamically changing Pareto-front.

A meta-learning prediction model of algorithm performance for continuous optimization problems

Algorithm selection and configuration is a challenging problem in the continuous optimization domain. An approach to tackle this problem is to develop a model that links landscape analysis measures and algorithm parameters to performance. This model can be then used to predict algorithm performance when a new optimization problem is presented. In this paper, we investigate the use of a machine learning framework to build such a model. We demonstrate the effectiveness of our technique using CMA-ES as a representative algorithm and a feed-forward backpropagation neural network as the learning strategy. Our experimental results show that we can build sufficiently accurate predictions of an algorithms expected performance. This information is used to rank the algorithm parameter settings based on the current problem instance, hence increasing the probability of selecting the best configuration for a new problem.

Algorithm selection for black-box continuous optimization problems

Selecting the most appropriate algorithm to use when attempting to solve a black-box continuous optimization problem is a challenging task. Such problems typically lack algebraic expressions, it is not possible to calculate derivative information, and the problem may exhibit uncertainty or noise. In many cases, the input and output variables are analyzed without considering the internal details of the problem. Algorithm selection requires expert knowledge of search algorithm efficacy and skills in algorithm engineering and statistics. Even with the necessary knowledge and skills, success is not guaranteed.In this paper, we present a survey of methods for algorithm selection in the black-box continuous optimization domain. We start the review by presenting Rices (1976) selection framework. We describe each of the four component spaces - problem, algorithm, performance and characteristic - in terms of requirements for black-box continuous optimization problems. This is followed by an examination of exploratory landscape analysis methods that can be used to effectively extract the problem characteristics. Subsequently, we propose a classification of the landscape analysis methods based on their order, neighborhood structure and computational complexity. We then discuss applications of the algorithm selection framework and the relationship between it and algorithm portfolios, hybrid meta-heuristics, and hyper-heuristics. The paper concludes with the identification of key challenges and proposes future research directions.

Exploratory Landscape Analysis of Continuous Space Optimization Problems Using Information Content

Data-driven analysis methods, such as the information content of a fitness sequence, characterize a discrete fitness landscape by quantifying its smoothness, ruggedness, or neutrality. However, enhancements to the information content method are required when dealing with continuous fitness landscapes. One typically employed adaptation is to sample the fitness landscape using random walks with variable step size. However, this adaptation has significant limitations: random walks may produce biased samples, and uncertainty is added because the distance between observations is not accounted for. In this paper, we introduce a robust information content-based method for continuous fitness landscapes, which addresses these limitations. Our method generates four measures related to the landscape features. Numerical simulations are used to evaluate the efficacy of the proposed method. We calculate the Pearson correlation coefficient between the new measures and other well-known exploratory landscape analysis measures. Significant differences on the measures between benchmark functions are subsequently identified. We then demonstrate the practical relevance of the new measures using them as class predictors on a machine learning model, which classifies the benchmark functions into five groups. Classification accuracy greater than 90% was obtained, with computational costs bounded between 1% and 10% of the maximum function evaluation budget. The results demonstrate that our method provides relevant information, at a low cost in terms of function evaluations.

Performance measures and particle swarm methods for dynamic multi-objective optimization problems

Introduction: Multiobjective optimization represents an important class of optimization techniques which have a direct implication for solving many real-world problems. In recent years, using evolutionary algorithms to solve multiobjective optimization problems, commonly known as EMO (Evolutionary Multi-objective Optimization), has gained rapid popularity. Since Evolutionary Algorithms (EAs) make use of a population of candidate solutions, a diverse set of optimal solutions so called Pareto-optimal solutions can be found within a single run. EAs offer a distinct advantage over many traditional optimization methods where multiple solutions must be found in multiple separate runs.

Extended Differential Grouping for Large Scale Global Optimization with Direct and Indirect Variable Interactions

Cooperative co-evolution is a framework that can be used to effectively solve large scale optimization problems. This approach employs a divide and conquer strategy, which decomposes the problem into sub-components that are optimized separately. However, solution quality relies heavily on the decomposition method used. Ideally, the interacting decision variables should be assigned to the same sub-component and the interdependency between sub-components should be kept to a minimum. Differential grouping, a recently proposed method, has high decomposition accuracy across a suite of benchmark functions. However, we show that differential grouping can only identify decision variables that interact directly. Subsequently, we propose an extension of differential grouping that is able to correctly identify decision variables that also interact indirectly. Empirical studies show that our extended differential grouping method achieves perfect decomposition on all of the benchmark functions investigated. Significantly, when our decomposition method is embedded in the cooperative co-evolution framework, it achieves comparable or better solution quality than the differential grouping method.

A hybrid of multiobjective Evolutionary Algorithm and HMM-Fuzzy model for time series prediction

In this paper, we introduce a new hybrid of Hidden Markov Model (HMM), Fuzzy Logic and multiobjective Evolutionary Algorithm (EA) for building a fuzzy model to predict non-linear time series data. In this hybrid approach, the HMMs log-likelihood score for each data pattern is used to rank the data and fuzzy rules are generated using the ranked data. We use multiobjective EA to find a range of trade-off solutions between the number of fuzzy rules and the prediction accuracy. The model is tested on a number of benchmark and more recent financial time series data. The experimental results clearly demonstrate that our model is able to generate a reduced number of fuzzy rules with similar (and in some cases better) performance compared with typical data driven fuzzy models reported in the literature.