Computational intelligence for evolving trading rules

Copyright © 2008 IEEEThis paper describes an adaptive computational intelligence system for learning trading rules. The trading rules are represented using a fuzzy logic rule base, and using an artificial evolutionary process the system learns to form rules that can perform well in dynamic market conditions. A comprehensive analysis of the results of applying the system for portfolio construction using portfolio evaluation tools widely accepted by both the financial industry and academia is provided.Adam Ghandar, Zbigniew Michalewicz, Martin Schmidt, Thuy-Duong Tô, and Ralf Zurbrug

Evolving temporal association rules with genetic algorithms

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of the proposed framework isolates target temporal itemsets in synthetic datasets. The Iterative Rule Learning method successfully discovers these targets in datasets with varying levels of difficulty

Considerations of the nature of the relationship between generalization and interpretability in evolutionary fuzzy systems

Performance out of sample is a clear determinant of the usefulness of any prediction model regardless of the application. Fuzzy knowledge base systems are also useful due to interpretability; this factor is often cited as an advantage over “black box” systems which make model verification by expert users more difficult. Here we examine additional advantages of interpretability for promoting general performance out side training data.Adam Ghandar and Zbigniew Michalewic

Using cellular evolution for diversification of the balance between accurate and interpretable fuzzy knowledge bases for classification

Recent work combining population based heuristics and flexible models such as fuzzy rules, neural networks, and others, has led to novel and powerful approaches in many problem areas. This study tests an implementation of cellular evolution for fuzzy rule learning problems and compares the results with other related approaches. The paper also examines characteristics of the cellular evolutionary approach in generating more diverse solutions in a multiobjective specification of the learning task, and finds that solutions seem to have useful properties that could enable anticipating out of sample performance. We consider a bi-objective problem of learning fuzzy classifiers that balance accuracy and interpretability requirements.Adam Ghandar, Zbigniew Michalewiczhttp://cec2011.org

An experimental study of multi-objective evolutionary algorithms for balancing interpretability and accuracy in fuzzy rulebase classifiers for financial prediction

This paper examines the advantages of simple models over more complex ones for financial prediction. This premise is examined using a genetic fuzzy framework. The interpretability of fuzzy systems is oftentimes put forward as a unique advantageous feature, sometimes to justify effort associated with using fuzzy classifiers instead of alternatives that can be more readily implemented using existing tools. Here we investigate if model interpretability can provide further benefits by realizing useful properties in computationally intelligent systems for financial modeling. We test an approach for learning momentum based strategies that predict price movements of the Bombay Stock Exchange (BSE). The paper contributes an experimental evaluation of the relationship between the predictive capability and interpretability of fuzzy rule based systems obtained using Multi- Objective Evolutionary Algorithms (MOEA.)Adam Ghandar and Zbigniew Michalewic

The relationship between model complexity and forecasting performance for computer intelligence optimization in finance

Abstract not availableAdam Ghandar, Zbigniew Michalewicz, Ralf Zurbrueg

A case for learning simpler rule sets with multiobjective evolutionary algorithms

Also has ISBN 3642225462 ; 9783642225468Fuzzy rules can be understood by people because of their specification in structured natural language. In a wide range of decision support applications in business, the interpretability of rule based systems is a distinguishing feature, and advantage over, possible alternate approaches that are perceived as "black boxes", for example in facilitating accountability. The motivation of this paper is to consider the relationships between rule simplicity (the key component of interpretability) and out-of-sample performance. Forecasting has been described as both art and science to emphasize intuition and experience aspects of the process: aspects of intelligence manifestly difficult to reproduce artificially. We explore, computationally, the widely appreciated forecasting "rule-of-thumb" expressed in Ockham's principle that "simpler explanations are more likely to be correct".Adam Ghandar, Zbigniew Michalewicz and Ralf Zurbruegghttp://dl.acm.org/citation.cfm?id=203281

Estimating the reproductive potential of offspring in evolutionary heuristics for combinatorial optimization problems

This paper proposes a metaheuristic selection technique for controlling the progress of an evolutionary algorithm (and possibly other heuristic search techniques) to manipulate and make use of the relationship between runtime and solution quality. The paper examines the idea that very rapid increases in initial fitness may lead to premature convergence and a reported solution that is less than optimal. We examine the advantages provided by this metaheuristic selection technique in solving two different combinatorial optimization problems: including a "toy" problem of finding magic squares and a more realistic vehicle routing problem (VRP) benchmark. The method is found to be useful for finding both higher quality solutions with a marginally longer algorithm run time and for obtaining lower quality solutions in a shorter time. Furthermore, the impact on the search results is similar for both the magic square and the VRP problem providing evidence the method is scalable to other problem domains, and therefore is potentially a relatively straight forward addition to many heuristic approaches that can add value by improving both runtime and solution quality.Eddy Parkinson, Adam Ghandar, Zbigniew Michalewicz and Andrew Tusonhttp://cec2011.org

Learning multi-criteria fuzzy rule based decision models for hedge fund management

Adam Ghandar, Zbigniew Michalewicz and Ralf Zurbruegghttp://www.smartframe.de/mic09/Home.htm

Pattern Puzzle: A Metaphor for Visualising Software Complexity Measures

Software systems have become increasingly complex over the years. Complexity metrics measures software complexity using real numbers. It is, however, hard to gain insight into different complexities by looking at these numbers. In this paper, we present a software complexity metaphor that uses a jigsaw puzzle. In particular, each component of a software system is modelled as a piece of a jigsaw puzzle. The problem complexity is modelled as a pattern on the surface of the piece, and the interconnection complexity as connectors between puzzle pieces. We demonstrate the benefits of this approach using case studies of the complexity measures of a real software system