A brief history of learning classifier systems: from CS-1 to XCS and its variants

© 2015, Springer-Verlag Berlin Heidelberg. The direction set by Wilson’s XCS is that modern Learning Classifier Systems can be characterized by their use of rule accuracy as the utility metric for the search algorithm(s) discovering useful rules. Such searching typically takes place within the restricted space of co-active rules for efficiency. This paper gives an overview of the evolution of Learning Classifier Systems up to XCS, and then of some of the subsequent developments of Wilson’s algorithm to different types of learning

Machine learning and personality traits: A disturbing contribution from the algorithmic culture to behavioral science

The time has come for behavioral scholars to benefit from the superior prediction accuracy of modern data mining practices over traditional data modeling. The current investigative study uses machine learning techniques to explore the prediction power of the HEXACO traits on work performance. To obtain reliable results, I employ the most prevailing machine learning algorithms in addition to logistic and multiple regression. The concomitant use of multiple prediction models that are grounded solidly in specific literature is applied to reveal the most accurate model for prediction purposes. One relevant methodological contribution of the present study is the employment a Random Forest-based heuristic method that computes the ratio of actual splits on a certain variable to the number of times that particular variable was selected as a candidate to split within the forest. By computing the order of importance of traits as job performance predictors, the current research illuminates the field with relevant and accurate information regarding the crucial role of humility, the strongest job performance predictor. Also, from a novel perspective and in light of Liebig's Law of the Minimum, the present study reveals a strong influence of relative proportions of traits (i.e., ratio between scores of traits) on job performance ratings. Interestingly the second most important predictor was found to be the ratio between scores on emotional stability and conscientiousness, followed by the ratio between scores on extraversion and openness to experience. In certain conditions, these results reveal that proportions between two different traits may be stronger predictors of job performance than individual traits. Taken all together, this research is the first academic study to use machine learning techniques on HEXACO personality scores to reveal job performance-related predictors with seamless high predictive accuracy. Indeed, the methodological and theoretical contributions obtained in the current study should be carefully examined by practitioners and scientists in order to pragmatically leverage the management research field

A grammar-based technique for genetic search and optimization

The genetic algorithm (GA) is a robust search technique which has been theoretically and empirically proven to provide efficient search for a variety of problems. Due largely to the semantic and expressive limitations of adopting a bitstring representation, however, the traditional GA has not found wide acceptance in the Artificial Intelligence community. In addition, binary chromosones can unevenly weight genetic search, reduce the effectiveness of recombination operators, make it difficult to solve problems whose solution schemata are of high order and defining length, and hinder new schema discovery in cases where chromosome-wide changes are required.;The research presented in this dissertation describes a grammar-based approach to genetic algorithms. Under this new paradigm, all members of the population are strings produced by a problem-specific grammar. Since any structure which can be expressed in Backus-Naur Form can thus be manipulated by genetic operators, a grammar-based GA strategy provides a consistent methodology for handling any population structure expressible in terms of a context-free grammar.;In order to lend theoretical support to the development of the syntactic GA, the concept of a trace schema--a similarity template for matching the derivation traces of grammar-defined rules--was introduced. An analysis of the manner in which a grammar-based GA operates yielded a Trace Schema Theorem for rule processing, which states that above-average trace schemata containing relatively few non-terminal productions are sampled with increasing frequency by syntactic genetic search. Schemata thus serve as the building blocks in the construction of the complex rule structures manipulated by syntactic GAs.;As part of the research presented in this dissertation, the GEnetic Rule Discovery System (GERDS) implementation of the grammar-based GA was developed. A comparison between the performance of GERDS and the traditional GA showed that the class of problems solvable by a syntactic GA is a superset of the class solvable by its binary counterpart, and that the added expressiveness greatly facilitates the representation of GA problems. to strengthen that conclusion, several experiments encompassing diverse domains were performed with favorable results

Learning with delayed reinforcement in an exploratory probabilistic logic neural network

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