The Anticipatory Classifier System and Genetic Generalization
Butz, M., Goldberg, D.E., Stolzmann, W. (2000)TR No.: 2000032 | Download PDF | Download PS
Abstract:
The anticipatory classifier system (ACS)
combines the learning classifier system framework with the learning theory of
anticipatory behavioral control. The result is an evolutionary system that
builds an environmental model and further applies reinforcement learning
techniques to form an optimal behavioral policy in the model. After providing
some background as well as outlining the objectives of the system, we explain
in […]
Posted: March 24th, 2000 under Genetic algorithms. Comments: none
An Algorithmic Description of XCS
Butz, M.V., Wilson, S.W. (2000)TR No.: 2000017 | Download PDF | Download PS
Abstract:
A concise description of the XCS classifier system’s
parameters, structures, and algorithms is presented
as an aid to research. The algorithms are written in modularly
structured pseudo code with accompanying explanations.
Related PostsOiling the Wheels of Change: The Role of Adaptive Automatic Problem Decomposition in Non–Stationary EnvironmentsA Genetic Algorithm for Developing Modular Product ArchitecturesThe Anticipatory Classifier System and […]
Posted: March 24th, 2000 under Genetic algorithms. Comments: none
Ordering messy genetic algorithm in C++
Knjazew, D. (2000)TR No.: 2000034 | Download PDF | Download PS
Abstract:
The OmeGA 1.0 implementation of the ordering messy genetic algorithm
in C++ is freely available from the IlliGAL anonymous ftp-site.
The implementation covers the basic features of the
OmeGA and provides three permutation problems for testing purposes.
This paper explains how to download, compile, and run the code.
Related PostsOMEGA - Ordering Messy GA : Solving Permutation Problems with the […]
Posted: March 20th, 2000 under Genetic algorithms. Comments: none
Probability-Enhanced Predictions in the Anticipatory Classifier System
Butz, M.V., Goldberg, D.E., Stolzmann, W. (2000)TR No.: 2000016 | Download PDF | Download PS
Abstract:
The Anticipatory Classifier System (ACS) recently showed many capabilities
new to the Learning Classifier System field. Due to its enhanced rule
structure with an effect part, it forms an internal environmental
representation, learns latently besides the common reward learning, and
can use many cognitive processes. This paper introduces a
probability-enhancement in the predictions of the ACS which
enables the system to […]
Posted: March 20th, 2000 under Genetic algorithms. Comments: none
Progress Toward Linkage Learning in Real-Coded
Tsutsui, S., Goldberg, D.E., Sastry, K. (2000)TR No.: 2000033 | Download PDF | Download PS
Abstract:
In recent years, many researchers have concentrated on using real-valued genes in genetic and evolutionary algorithms (GEAs). Previous studies have proposed simplex crossover (SPX) for real-coded GAs. SPX has several good characteristics and works well on various test functions. However, SPX fails on functions that consist of tightly linked sub-functions. On those functions, SPX […]
Posted: March 16th, 2000 under Genetic algorithms. Comments: none
Time complexity of genetic algorithms on exponentially scaled problems
Lobo, F.G., Goldberg, D.E., Pelikan, M. (2000)TR No.: 2000015 | Download PDF | Download PS
Abstract:
This paper gives a theoretical and empirical analysis of the time
complexity of genetic algorithms (GAs) on problems with exponentially scaled
building blocks. It is important to study GA performance on this type of
problems because one of the difficulties that GAs are generally faced with
is due to the low scaling or low salience of some building blocks.
The […]
Posted: March 16th, 2000 under Genetic algorithms. Comments: none
Mining Oblique Data with XCS
Wilson, S.W. (2000)TR No.: 2000028 | Download PDF | Download PS
Abstract:
The classifier system XCS was investigated for data mining applications where the dataset discrimination surface (DS) is generally oblique to the attribute axes. Despite the classifiers’ hyper-rectangular predicates, XCS reached 100% performance on synthetic problems with diagonal DS’s and, in a train/test experiment, competitive performance on the Wisconsin Breast Cancer dataset. Final classifiers in an […]
Posted: March 12th, 2000 under Genetic algorithms. Comments: none
Investigating Generalization in the Anticipatory Classifier System
Butz, M.V., Goldberg, D.E., Stolzmann, W. (2000)TR No.: 2000014 | Download PDF | Download PS
Abstract:
Recently, a genetic algorithm (GA) was introduced to the Anticipatory Classifier System (ACS) which surmounted the occasional problem of over-specification of rules. This paper investigates the resulting generalization capabilities further by monitoring in detail the performance of the ACS in the highly challenging multiplexer
task. Moreover, by comparing the ACS to XCS in this task it […]
Posted: March 12th, 2000 under Genetic algorithms. Comments: none
Network random keys - a tree network representation scheme for genetic and evolutionary algorithms
Rothlauf, F., Goldberg, D.E., Heinzl, A. (2000)TR No.: 2000031 | Download PDF | Download PS
Abstract:
When using genetic and evolutionary algorithms for the design of network structures, a good choice of the representation scheme for the construction of the genotype is important for the performance of the algorithm.
One of the most common representation schemes for networks is the characteristic vector representation. However, with encoding tree networks, and using crossover […]
Posted: March 8th, 2000 under Genetic algorithms. Comments: none
Genetic Algorithms, Clustering, and the Breaking of Symmetry
Pelikan, M., Goldberg, D.E. (2000)TR No.: 2000013 | Download PDF | Download PS
Abstract:
This paper introduces clustering as a tool to improve the effects of recombination and incorporate niching in evolutionary algorithms. Instead of processing the entire set of parent solutions, the set is first clustered and the solutions in each of the clusters are processed separately. This alleviates the problem of symmetry which is often a major […]
Posted: March 8th, 2000 under Genetic algorithms. Comments: none