Classifier Systems and Genetic Algorithms
Classifier systems are massively parallel, message-passing, rule-based systems that learn through credit assignment (the bucket brigade algorithm) and rule discovery (the genetic algorithm). They typically operate in environments that exhibit one or more of the following characteristics: (1) perpetually novel events accompanied by large amounts of noisy or irrelevant data; (2) continual, often real-time, requirements for action; (3) implicitly or inexactly defined goals; and (4) sparse payoff or reinforcement obtainable only through long action sequences. Classifier systems are designed to absorb new information continuously from such environments, devising sets of competing hypotheses (expressed as rules) without disturbing significantly capabilities already acquired. This paper reviews the definition, theory, and extant applications of classifier systems, comparing them with other machine learning techniques, and closing with a discussion of advantages, problems, and possible extensions of classifier systems.

title: Classifier prediction based on tile coding
abstract: This paper introduces XCSF extended with tile coding prediction: each classifier implements a tile coding approximator; the genetic algorithm is used to adapt both classifier conditions (i.e., to partition the problem) and the parameters of each approximator; thus XCSF evolves an ensemble of tile coding approximators instead of the typical monolithic approximator used in reinforcement learning. The paper reports a comparison between (i) XCSF with tile coding prediction and (ii) plain tile coding. The results show that XCSF with tile coding always reaches optimal performance, it usually learns as fast as the best parametrized tile coding, and it can be faster than the typical tile coding setting. In addition, the analysis of the evolved tile coding ensembles shows that XCSF actually adapts local approximators following what is currently considered the best strategy to adapt the tile coding parameters in a given problem.

title: Population sizing for entropy-based model building in discrete estimation of distribution algorithms
abstract: This paper proposes a population-sizing model for entropy-based model building in discrete estimation of distribution algorithms. Specifically, the population size required for building an accurate model is investigated. The effect of selection pressure on population sizing is also preliminarily incorporated. The proposed model indicates that the population size required for building an accurate model scales as Θ(m log m), where m is the number of substructures of the given problem and is proportional to the problem size. Experiments are conducted to verify the derivations, and the results agree with the proposed model.

title: Substructural Surrogates for Learning Decomposable Classification Problems
abstract: This paper presents a learning methodology based on a substructural classification model to solve decomposable classification problems. The proposed method consists of three important components: (1) a structural model, which represents salient interactions between attributes for a given data, (2) a surrogate model, which provides a functional approximation of the output as a function of attributes, and (3) a classification model, which predicts the class for new inputs. The structural model is used to infer the functional form of the surrogate. Its coefficients are estimated using linear regression methods. The classification model uses a maximally-accurate, least-complex surrogate to predict the output for given inputs. The structural model that yields an optimal classification model is searched using an iterative greedy search heuristic. Results show that the proposed method successfully detects the interacting variables in hierarchical problems, groups them in linkages groups, and builds maximally accurate classification models. The initial results on non-trivial hierarchical test problems indicate that the proposed method holds promise and also shed light on several improvements to enhance the capabilities of the proposed method.

title: Construction of high-order deceptive functions using low-order Walsh coefficients
abstract: This paper constructs deceptive functions over bit strings of any length using low-order Walsh coefficients. Specifically, a partially deceptive construct is achieved with coefficients no higher than second order, and a fully deceptive construct is achieved with coefficients no higher than third order. Previous work on deception tacitly assumed that the very highest order Walsh coefficient, the order-l coefficient in a length-l problem, must be non-zero to achieve full deception, that condition where all schemata of orderl−1 or less containing the complement of the global optimum are superior to their competitors. The constructions of this paper show such assumptions to be false and lead to a more general theory of functions whose value only depends on the number of ones (or zeros) in the function's argument, so-calledfunctions of unitation. These results also help shed some light on Tanese's puzzling results on functions that contained large numbers of Walsh terms of fixed order.

title: Hierarchical Problem Solving and the Bayesian Optimization Algorithm
abstract: The paper discusses three major issues. First, it discusses why it makes sense to approach problems in a hierarchical fashion. It defines the class of hierarchically decomposable functions that can be used to test the algorithms that approach problems in this fashion. Finally, the Bayesian optimization algorithm (BOA) is extended in order to solve the proposed class of problems.

title: Teching PL/I using a microcomputer
abstract: Until the Fall 1983 semester, the introductory programming course at Brooklyn College had been taught (in the PL/C dialect of PL/I) using keypunch equipment in a batch environment. However, that term two experimental sections used IBM Personal Computer (PC's) instead of keypunches. The experiment was expanded in the Spring 1984 semester to include almost one third of all sections of the introductory course, and then expanded again in the Fall 1984 semester to all sections of the first PL/I course, plus three sections of second-level courses. Although the PC's were quite easy to use, the PL/I-86 PC-DOS compiler produced by Digital Research caused many problems. A number of advantages and disadvantages of the experiment are discussed. Other implications of the use of PC's are also noted, including the possible change of language and the extension to more advanced courses in the curriculum.