Chances and Marketing: On-line Conversation Analysis for Creative Scenario Discussion
Llorà, X., Goldberg, D. E., Ohsawa, Y., Ohnishi, K., Tamura, H., Washida, Y., Yoshikawa, M. (2004)TR No.: 2004025 | Download PDF | Download PS
Abstract:
Creativity protocols and methodologies tend to be time consuming if
applied manually. This paper presents how innovation support technologies
can be applied to collaborative scenario creation and discussion. The
fusion of change discovery, genetics algorithms, and computer-supported
collaborative tools, allow a group of participants in a creative processes
to have a pervasive access to reflection feedback about the scenario to
be […]
Posted: May 8th, 2004 under Genetic algorithms. Comments: none
Knowledge Extraction and Problem Structure Identification in XCS
Butz, M. V., Lanzi, P. L., Llorà, X., Goldberg, D. E. (2004)TR No.: 2004024 | Download PDF | Download PS
Abstract:
XCS has been shown to solve hard problems in a machine-learning
competitive way. Recent theoretical advancements show that the
system can scale-up polynomially in the problem complexity and
problem size given the problem is a k-DNF with certain properties.
This paper addresses two major issues in XCS: (1) knowledge
extraction and (2) structure identification. Knowledge extraction
addresses the issue of mining […]
Posted: May 4th, 2004 under Genetic algorithms. Comments: none
Learning Classifier Systems for Hyperspectral Images Processing
Quirin, A., Korczak, J., Butz, M. V., Goldberg, D. E. (2004)TR No.: 2004023 | Download PDF | Download PS
Abstract:
In this article, two learning classifier system based classification
techniques are described to classify remote sensing images. Usually, these
images contain voluminous, complex, and sometimes erroneous and noisy
data. The first approach implements ICU, an evolutionary rule discovery
system, generating simple and robust rules. The second approach applies
the real-valued accuracy-based classification system XCSR. The two
algorithms are detailed and validated […]
Posted: April 24th, 2004 under Genetic algorithms. Comments: none
Speeding-up Pittsburgh Learning Classifier Systems: Modeling Time and Accuracy
Bacardit, J., Goldberg, D. E., Butz, M. V., Llorà, X., Garrell J. M. (2004)TR No.: 2004022 | Download PDF | Download PS
Abstract:
Windowing methods are useful techniques to reduce the computational cost of
Pittsburgh-style genetic-based machine learning
techniques. If used properly, they additionally can be used
to improve the classification accuracy of the system. In this
paper we develop a theoretical framework for a windowing scheme called
ILAS, developed previously by the authors. The framework allows us
to approximate the degree of windowing […]
Posted: April 20th, 2004 under Genetic algorithms. Comments: none
Toward a Cognitive Sequence Learner: Hierarchy, Self-Organization, and Top-down Bottom-up Interaction
Butz, M. V. (2004)TR No.: 2004021 | Download PDF | Download PS
Abstract:
This paper introduces a hierarchical learning architecture that grows
online an adaptive problem representation from scratch. The representation extracts frequent perceptual patterns representing them in a layered hierarchy where neural activity in higher layers is initiated bottom-up by firing neurons in lower layers and, vice versa, firing neurons in higher layers predispose activity and provide reinforcement […]
Posted: April 16th, 2004 under Genetic algorithms. Comments: none
Efficiency Enhancement of Probabilistic Model Building Genetic Algorithms
Sastry, K., Goldberg, D. E., Pelikan, M. (2004)TR No.: 2004020 | Download PDF | Download PS
Abstract:
This paper presents two different efficiency-enhancement techniques for probabilistic model building genetic algorithms. The first technique proposes the use of a mutation operator which performs local search in the sub-solution neighborhood identified through the probabilistic model. The second technique proposes building and using an internal probabilistic model of the fitness along with the probabilistic model […]
Posted: April 12th, 2004 under Genetic algorithms. Comments: none
Genetic Programming for Multiscale Modeling
Sastry, K., Johnson, D.D., Goldberg, D.E., Bellon, P. (2004)TR No.: 2004019 | Download PDF | Download PS
Abstract:
We propose the use of genetic programming (GP)—a genetic algorithm
that evolves computer programs—for bridging simulation methods
across multiple scales of time and/or length. The effectiveness of
genetic programming in multiscale simulation is demonstrated using two
illustrative, non-trivial case studies in science and engineering. The
first case is multi-timescale materials kinetics modeling, where
genetic programming is used to symbolically regress a […]
Posted: April 8th, 2004 under Genetic algorithms. Comments: none
Extending the Scalability of Linkage Learning Genetic Algorithms: Theory and Practice
Chen, Y.-p. (2004)TR No.: 2004018 | Download PDF | Download PS
Abstract:
There are two primary objectives of this dissertation. The first goal is to
identify certain limits of genetic algorithms that use only fitness for
learning genetic linkage. Both an explanatory theory and experimental
results to support the theory are provided. The other goal is to propose a
better design of the linkage learning genetic algorithm. After understanding
the cause of […]
Posted: April 4th, 2004 under Genetic algorithms. Comments: none
Hierarchical Classification Problems Demand Effective Building Block Identification and Processing in LCSs
Butz, M. V., Goldberg, D. E. (2004)TR No.: 2004017 | Download PDF | Download PS
Abstract:
This paper introduces a class of hierarchically structured classification problems that call for effective building block identification and processing in XCS and learning classifier systems
in general.
Related PostsExtracted Global Structure Makes Local Building Block Processing Effective in XCSAutomated Global Structure Extraction For Effective Local Building Block Processing in XCSLinkage learning via probabilistic modeling in the ECGA
Posted: March 24th, 2004 under Genetic algorithms. Comments: none
Dependency Structure Matrix Analysis: Off-line Utility of the Dependency Structure Matrix Genetic Algorithm
Yu, T.-L., Goldberg D. E (2004)TR No.: 2004016 | Download PDF | Download PS
Abstract:
This paper investigates the off-line use of the dependency
structure matrix genetic algorithm (DSMGA). In particular, a
problem-specific crossover operator is design by performing
dependency structure matrix (DSM) analysis. The advantages and
disadvantages of such an off-line use are discussed. Two schemes
that helps the off-line usage are proposed. Finally, those
off-line schemes are demonstrated by DSMGA on MaxTrap functions.
Related PostsA […]
Posted: March 20th, 2004 under Genetic algorithms. Comments: none