Gradient Descent Methods in Learning Classifier Systems
Butz, M. V., Goldberg, D. E., Lanzi, P.-L. (2003)TR No.: 2003028 | Download PDF | Download PS
Abstract:
The accuracy-based XCS classifier system has been shown to solve typical
data mining problems in a machine-learning competitive way. However,
successful applications in multistep problems, modeled by a Markov
decision process, were restricted to very small problems. Until now, the
temporal difference learning technique in XCS was based on deterministic
updates. However, since a prediction is actually generated by a […]
Posted: May 20th, 2003 under Genetic algorithms. Comments: none
Strong, Stable, and Reliable Fitness Pressure in XCS due to Tournament Selection
Butz, M. V., Sastry, K., Goldberg, D. E. (2003)TR No.: 2003027 | Download PDF | Download PS
Abstract:
Recent analysis of the XCS classifier system have shown that successful
genetic learning strongly depends on the amount of fitness pressure
towards accurate classifiers. Since the traditionally used proportionate
selection is dependent on fitness scaling and fitness distribution, the
resulting evolutionary fitness pressure may be neither stable nor
sufficiently strong. Thus, we apply tournament selection to XCS. In
particular, we exhibit […]
Posted: May 16th, 2003 under Genetic algorithms. Comments: none
An Adaptive Sampling Scheme for Genetic Algorithms on the Sampled OneMax Problem
Yu, T.-L., Chen, Y.-P., Goldberg, D. E., Chen, J.-H. (2003)TR No.: 2003026 | Download PDF | Download PS
Abstract:
This paper proposes an adaptive sampling scheme for genetic algorithms. The adaptive sampling scheme is tested on the sampled OneMax problem. The results suggest that through this scheme, speed-up is obtained for problems with non-uniformly scaled building blocks (BBs). For problems
with uniformly scaled BBs, the proposed adaptive sampling scheme does not give speed-up but still […]
Posted: May 12th, 2003 under Genetic algorithms. Comments: none
Convergence Time for the Linkage Learning Genetic Algorithm
Chen, Y.-P., Goldberg, D. E. (2003)TR No.: 2003025 | Download PDF | Download PS
Abstract:
This paper identifies the sequential behavior of the linkage learning
genetic algorithm (LLGA), introduces the tightness time model for a single
building block, and develops the connection between sequential behavior and
the tightness time model. By integrating the first-building-block model
based on sequential behavior, the tightness time model, and the connection
between these two models, a convergence time model is […]
Posted: May 8th, 2003 under Genetic algorithms. Comments: none
A Genetic Algorithm for Developing Modular Product Architectures
Yu, T.-L., Yassine, A., Goldberg, D. E. (2003)TR No.: 2003024 | Download PDF | Download PS
Abstract:
The architecture of a product is determined by both the elements that compose the product and the way in which they interact with each other. In this paper, we use the design structure matrix (DSM) as a tool to capture this architecture. […]
Posted: May 4th, 2003 under Genetic algorithms. Comments: none
Documentation of XCS+TS C-Code 1.2
Butz, M. V. (2003)TR No.: 2003023 | Download PDF | Download PS
Abstract:
This is the documentation of the XCS 1.2 C-code released on the IlliGAL web-page. The code includes the option to apply tournament selection as well as several other new features in comparison to the XCS1.1 release. Moreover, XCS parameters as well as experimental settings can be specified in a parameter file so that recompiling is […]
Posted: April 24th, 2003 under Genetic algorithms. Comments: none
Using Edge Histogram Models to Solve Permutation Problems with Probabilistic Model-Building Genetic Algorithms
Tsutsui, S., Pelikan, M., Goldberg, D. E. (2003)TR No.: 2003022 | Download PDF | Download PS
Abstract:
Recently, there has been a growing interest in probabilistic model-building genetic algorithms (PMBGAs), which replace traditional variation operators of genetic and evolutionary algorithms by building and sampling a probabilistic model of promising solutions. In this paper we propose a PMBGA that uses edge histogram based sampling algorithms (EHBSAs) to solve problems with candidate solutions represented […]
Posted: April 20th, 2003 under Genetic algorithms. Comments: none
Bayesian Optimization Algorithms for Multiobjective and Hierarchically Difficult Problems
Khan, N. (2003)TR No.: 2003021 | Download PDF | Download PS
Abstract:
In the last two decades significant progress has been made in the
theory and design of competent genetic algorithms—genetic algorithms
(GAs) that solve hard problems quickly, reliably, and accurately
(Goldberg, 2002). In contrast to the first generation GAs which use
fixed recombination operators, competent GAs employ recombination
operators that adapt linkages. Competent GAs have been shown to solve
problems of bounded […]
Posted: April 16th, 2003 under Genetic algorithms. Comments: none
Designing Efficient Genetic and Evolutionary Algorithm Hybrids
Sinha, A. (2003)TR No.: 2003020 | Download PDF | Download PS
Abstract:
Genetic algorithms (GAs) are being increasingly employed to solve a wide range of problems in search and optimization. Most real-world applications use GAs in combination with domain specific methods to achieve superior performance. Such combinations, often referred to as hybrids, stand to gain much from a systems-level framework for efficiently combining global […]
Posted: April 12th, 2003 under Genetic algorithms. Comments: none
Robust and Scalable Black-Box Optimization, Hierarchy, and Ising Spin Glasses
Pelikan, M., Goldberg, D.E., Ocenasek, J., Trebst, S. (2003)TR No.: 2003019 | Download PDF | Download PS
Abstract:
One of the most important challenges in computational optimization is the design of advanced black-box optimization techniques that would enable automated, robust, and scalable solution to challenging optimization problems. This paper presents an advanced black-box optimizer—the hierarchical Bayesian optimization algorithm (hBOA)—that combines techniques of genetic and evolutionary computation, machine learning, and statistics to create a […]
Posted: April 8th, 2003 under Genetic algorithms. Comments: none