Analysis and Improvement of Fitness Exploitation in XCS: Bounding Models, Tournament Selection, and Bilateral Accuracy
Butz, M. V., Goldberg, D. E., Tharakunnel, K. (2002)TR No.: 2002027 | Download PDF | Download PS
Abstract:
The evolutionary learning mechanism in XCS strongly depends on its accuracy-based fitness approach. The approach is meant to result in an
evolutionary drive from classifiers of low accuracy to those of high accuracy. Since, given inaccuracy, lower specificity often corresponds to lower accuracy fitness pressure most often also results in a pressure towards higher specificity. Moreover, […]
Posted: May 16th, 2002 under Genetic algorithms. Comments: none
Discovering Deep Building Blocks for Competent Genetic Algorithms Using Chance Discovery via KeyGraphs
Goldberg, D. E., Sastry, K., Ohsawa, Y. (2002)TR No.: 2002026 | Download PDF | Download PS
Abstract:
In this paper, we see whether chance discovery in the form of KeyGraphs can be used to reveal deep building blocks to competent genetic algorithms, thereby speeding innovation in particularly difficult problems. On an intellectual level, showing the connection between KeyGraphs and genetic algorithms as related pieces of the innovation puzzle is both scientifically and […]
Posted: May 12th, 2002 under Genetic algorithms. Comments: none
Redundant Representations in Evolutionary Computation
Rothlauf, F., Goldberg, D. E. (2002)TR No.: 2002025 | Download PDF | Download PS
Abstract:
This paper investigates how the use of redundant representations influences the performance of genetic and evolutionary algorithms. Representations are redundant if the number of genotypes exceeds the number of phenotypes. A distinction is made between synonymously and non-synonymously redundant representations. Representation are synonymously redundant if the genotypes that represent the same phenotype are very similar […]
Posted: May 8th, 2002 under Genetic algorithms. Comments: none
XCS: Accuracy and Successful Generalizations
Tharakunnel, K. K., Butz, M. V., Goldberg, D. E. (2002)TR No.: 2002022 | Download PDF | Download PS
Abstract:
There has been much work in the past few years exhibiting success of the accuracy based XCS classifier system. At the same time, though, there have been other results that suggest the ill-performance of XCS. Although several studies on XCS are available there is little work explaining the negative results. This paper investigates one particular […]
Posted: May 4th, 2002 under Genetic algorithms. Comments: none
Solving sequence problems by building and sampling edge histograms
Tsutsui, S., Goldberg, D. E., Pelikan, M. (2002)TR No.: 2002024 | Download PDF | Download PS
Abstract:
Recently, there has been a growing interest in developing evolutionary algorithms based on probabilistic modeling. In this scheme, the offspring population is generated according to the estimated
probability density model of the parent instead of using recombination and mutation operators. In this paper, we have proposed probabilistic model-building genetic algorithms (PMBGAs) in permutation representation domain using […]
Posted: April 24th, 2002 under Genetic algorithms. Comments: none
Bayesian optimization algorithm: From single level to hierarchy
Pelikan, M. (2002)TR No.: 2002023 | Download PDF | Download PS
Abstract:
There are four primary goals of this dissertation. First, design a competent optimization algorithm capable of learning and exploiting appropriate problem decomposition by sampling and evaluating candidate solutions. Second, extend the proposed algorithm to enable the use of hierarchical decomposition as opposed to decomposition on only a single level. Third, design a class of difficult […]
Posted: April 20th, 2002 under Genetic algorithms. Comments: none
Time Continuation in Genetic Algorithms
Srivastava R. P. (2002)TR No.: 2002021 | Download PDF | Download PS
Abstract:
Given a sufficiently large population size and run duration, genetic lgorithms yield satisfactory results for a range of problems. However,
when resources and time are limited, as is the case with any real-world
problem, it is imperative to use those resources judiciously.
This master’s thesis studies the behavior and performance of genetic
algorithms under the influence of multiple-epoch runs […]
Posted: April 16th, 2002 under Genetic algorithms. Comments: none
Tournament Selection in XCS
Butz, M. V., Sastry, K., Goldberg, D. E. (2002)TR No.: 2002020 | Download PDF | Download PS
Abstract:
Selection in the accuracy-based learning classifier system XCS, introduced by Wilson in 1995, has always been done by the means of proportionate selection. Although it is known from GA literature that proportionate selection is subject to many pitfalls, the LCS community adhered to proportionate selection. In XCS, the accuracy-based fitness is scaled which made proportionate […]
Posted: April 12th, 2002 under Genetic algorithms. Comments: none
Generalized State Values in an Anticipatory Learning Classifier System
Butz, M. V., Goldberg, D. E. (2002)TR No.: 2002019 | Download PDF | Download PS
Abstract:
This paper introduces generalized state values to the anticipatory learning classifier system ACS2. Previous studies with ACS2 showed that the system reliably evolves a generalized predictive model in typical Markov decision process (MDP). The predictive model approximates the state transition function of the MDP in a compact, generalized form. However, it was also shown that […]
Posted: April 8th, 2002 under Genetic algorithms. Comments: none
State Value Learning with an Anticipatory Learning Classifier System in a Markov Decision Process
Butz, M. V. (2002)TR No.: 2002018 | Download PDF | Download PS
Abstract:
This paper addresses the combination of an online generalizing model learner with a state value learner in a Markov decision process (MDP). The model learner evolves online a generalized representation of the MDP’s state transition function. The learned model is called predictive model.
State values are evaluated by the means of the evolving predictive model
representation. State […]
Posted: April 4th, 2002 under Genetic algorithms. Comments: none