Sub-Structural Niching in Non-Stationary Environments
Sastry, K., Abbass, H. A., Goldberg, D. E. (2004)TR No.: 2004035 | Download PDF | Download PS
Abstract:
Niching enables a genetic algorithm (GA) to maintain diversity in a population. It is particularly useful when the problem has multiple optima where the aim is to find all or as many as possible of these optima. When the fitness landscape of a problem changes overtime, the problem is called non–stationary, dynamic or time–variant problem. […]
Posted: June 24th, 2004 under Genetic algorithms. Comments: none
Rule-based Evolutionary Online Learning Systems: Learning Bounds, Classification, and Prediction
Butz, M. V. (2004)TR No.: 2004034 | Download PDF | Download PS
Abstract:
Rule-based evolutionary online learning systems, often referred to as
Michigan-style learning classifier systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems.
LCSs combine the strength of reinforcement learning with the generalization capabilities of genetic algorithms promising a flexible, online generalizing, solely reinforcement dependent learning
system.
However, despite several initial successful applications […]
Posted: June 20th, 2004 under Genetic algorithms. Comments: none
Bounding the Population Size to Ensure Niche Support in XCS
Butz, M. V., Goldberg, D. E., Lanzi, P. L., Sastry, K. (2004)TR No.: 2004033 | Download PDF | Download PS
Abstract:
Michigan-style learning classifier systems evolve a problem solution maintaining a set of sub-solutions distributed to potentially overlapping problem subspaces. Together, the set of sub-solutions, represented by rules, represents the complete problem solution. An obvious challenge for such an evolving, distributed knowledge representation is the continuous support of all problem subspaces, that is, the niche support. […]
Posted: June 16th, 2004 under Genetic algorithms. Comments: none
Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule
Bacardit, J., Goldberg, D. E., Butz, M. V. (2004)TR No.: 2004032 | Download PDF | Download PS
Abstract:
An interesting feature of encoding the individuals of a Pittsburgh Learning Classifier System as a decision list is the emergent generation of a default rule. However, performance of the system is strongly tied to the learning system choosing the correct class for this default rule. In this paper we experimentally study the use of an […]
Posted: June 12th, 2004 under Genetic algorithms. Comments: none
Online Adaptation in Learning Classifier Systems: Stream Data Mining
Abbass, H. A., Bacardit, J., Butz, M. V., Llorà, X. (2004)TR No.: 2004031 | Download PDF | Download PS
Abstract:
In data mining, concept drift refers to the phenomenon that the underlying model (or concept) is changing over time. The aim of this paper is twofold. First, we propose a fundamental characterization and quantification of different types of concept drift. The proposed theory enables a rigorous investigation of learning system performance on streamed data. In […]
Posted: June 8th, 2004 under Genetic algorithms. Comments: none
Data Mining in Learning Classifier Systems: Comparing XCS with GAssist
Bacardit, J., Butz, M. V. (2004)TR No.: 2004030 | Download PDF | Download PS
Abstract:
This paper compares performance of the Pittsburgh-style system GAssist with the Michigan-style system XCS on several datamining problems. Our analysis shows that both systems are suitable for datamining but have different advantages and disadvantages. The study does not only reveal important differences between the two systems but also suggests several structural properties of the underlying […]
Posted: June 4th, 2004 under Genetic algorithms. Comments: none
Oiling the Wheels of Change: The Role of Adaptive Automatic Problem Decomposition in Non–Stationary Environments
Abbass, H. A., Sastry, K., Goldberg, D. E. (2004)TR No.: 2004029 | Download PDF | Download PS
Abstract:
Genetic algorithms (GAs) that solve hard problems quickly, reliably and accurately are called competent GAs. When the fitness landscape of a problem changes overtime, the problem is
called non—stationary, dynamic or time—variant problem. This
paper investigates the use of competent GAs for optimizing
non–stationary optimization problems. More specifically, we use
an information theoretic approach based on the minimum […]
Posted: May 24th, 2004 under Genetic algorithms. Comments: none
Population Sizing for Genetic Programming Based Upon Decision Making
Sastry, K, O'reilly, U.-M., Goldberg, D. E. (2004)TR No.: 2004028 | Download PDF | Download PS
Abstract:
This paper derives a population sizing relationship for genetic programming (GP). Following the population-sizing derivation for genetic algorithms in Goldberg, Deb, and Clark (1992), it
considers building block decision making as a key facet. The analysis yields a GP-unique relationship because it has to account for bloat and for the fact that GP solutions […]
Posted: May 20th, 2004 under Genetic algorithms. Comments: none
Anticipation for Learning, Cognition, and Education
Butz, M. V. (2004)TR No.: 2004027 | Download PDF | Download PS
Abstract:
Predictions, desires, or intentions have recently shown to strongly influence behavior, adaptation, and learning. These anticipations influence behavior mediating decision making and action execution as well as attention. Although it is not the future itself that influences the present but the anticipated future states or future properties, the difference to purely stimulus driven behavior and […]
Posted: May 16th, 2004 under Genetic algorithms. Comments: none
Discovering Chance Scenarios using Small-World KeyGraphs and Evolutionary Computation
Llorà, X., Matsumura, N., Goldberg, D. E., Ohsawa, Y., Ohnishi, K., Gonzales, A. (2004)TR No.: 2004026 | Download PDF | Download PS
Abstract:
A successful process of chance discovery using the visual maps proposed
by KeyGraphs requires the usage of graphs with an appropriate degree of
complexity. Complex KeyGraphs often prevent users from discovering
chances because of the difficulties of interpretation. On the other hand,
overly simplistic KeyGraphs seldom includes a chance because of the
sparseness of information. In a useful KeyGraphs the […]
Posted: May 12th, 2004 under Genetic algorithms. Comments: none