How XCS Evolves Accurate Classifiers

MV Butz; T Kovacs; PL Lanzi; SW Wilson

How XCS Evolves Accurate Classifiers

MV Butz, T Kovacs, PL Lanzi, SW Wilson

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

Abstract

Due to the accuracy based fitness approach, the ultimate goal for XCS is the evolution of a compact, complete, and accurate payoff mapping of an environment. This paper investigates what causes the XCS classifier system to evolve accurate classifiers. The investigation leads to two challenges for XCS, the covering challenge and the schema challenge. Both challenges are revealed theoretically and experimentally. Furthermore, the paper provides suggestions for overcoming the challenges as well as investigates environmental properties that can help XCS to overcome the challenges autonomously. Along those lines, a deeper insight into how to set the initial parameter values in XCS is provided.

Translated title of the contribution	How XCS Evolves Accurate Classifiers
Original language	English
Title of host publication	Unknown
Editors	Lee Spector et al.
Publisher	Morgan Kaufmann
Pages	927 - 934
Number of pages	7
Publication status	Published - Jul 2001

Bibliographical note

Conference Proceedings/Title of Journal: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)

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@inproceedings{22a2848628124549bec917b048c45513,

title = "How XCS Evolves Accurate Classifiers",

abstract = "Due to the accuracy based fitness approach, the ultimate goal for XCS is the evolution of a compact, complete, and accurate payoff mapping of an environment. This paper investigates what causes the XCS classifier system to evolve accurate classifiers. The investigation leads to two challenges for XCS, the covering challenge and the schema challenge. Both challenges are revealed theoretically and experimentally. Furthermore, the paper provides suggestions for overcoming the challenges as well as investigates environmental properties that can help XCS to overcome the challenges autonomously. Along those lines, a deeper insight into how to set the initial parameter values in XCS is provided.",

author = "MV Butz and T Kovacs and PL Lanzi and SW Wilson",

note = "Conference Proceedings/Title of Journal: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)",

year = "2001",

month = jul,

language = "English",

pages = "927 -- 934",

editor = "al., {Lee Spector et}",

booktitle = "Unknown",

publisher = "Morgan Kaufmann",

}

TY - GEN

T1 - How XCS Evolves Accurate Classifiers

AU - Butz, MV

AU - Kovacs, T

AU - Lanzi, PL

AU - Wilson, SW

N1 - Conference Proceedings/Title of Journal: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)

PY - 2001/7

Y1 - 2001/7

N2 - Due to the accuracy based fitness approach, the ultimate goal for XCS is the evolution of a compact, complete, and accurate payoff mapping of an environment. This paper investigates what causes the XCS classifier system to evolve accurate classifiers. The investigation leads to two challenges for XCS, the covering challenge and the schema challenge. Both challenges are revealed theoretically and experimentally. Furthermore, the paper provides suggestions for overcoming the challenges as well as investigates environmental properties that can help XCS to overcome the challenges autonomously. Along those lines, a deeper insight into how to set the initial parameter values in XCS is provided.

AB - Due to the accuracy based fitness approach, the ultimate goal for XCS is the evolution of a compact, complete, and accurate payoff mapping of an environment. This paper investigates what causes the XCS classifier system to evolve accurate classifiers. The investigation leads to two challenges for XCS, the covering challenge and the schema challenge. Both challenges are revealed theoretically and experimentally. Furthermore, the paper provides suggestions for overcoming the challenges as well as investigates environmental properties that can help XCS to overcome the challenges autonomously. Along those lines, a deeper insight into how to set the initial parameter values in XCS is provided.

M3 - Conference Contribution (Conference Proceeding)

SP - 927

EP - 934

BT - Unknown

A2 - al., Lee Spector et

PB - Morgan Kaufmann

ER -

How XCS Evolves Accurate Classifiers

Abstract

Bibliographical note

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