TY - GEN
T1 - Self organizing classifiers and niched fitness
AU - Vargas, Danilo V.
AU - Takano, Hirotaka
AU - Murata, Junichi
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - Learning classifier systems are adaptive learning systems which have been widely applied in a multitude of application domains. However, there are still some generalization problems unsolved. The hurdle is that fitness and niching pressures are difficult to balance. Here, a new algorithm called Self Organizing Classifiers is proposed which faces this problem from a different perspective. Instead of balancing the pressures, both pressures are separated and no balance is necessary. In fact, the proposed algorithm possesses a dynamical population structure that self-organizes itself to better project the input space into a map. The niched fitness concept is defined along with its dynamical population structure, both are indispensable for the understanding of the proposed method. Promising results are shown on two continuous multi-step problems. One of which is yet more challenging than previous problems of this class in the literature.
AB - Learning classifier systems are adaptive learning systems which have been widely applied in a multitude of application domains. However, there are still some generalization problems unsolved. The hurdle is that fitness and niching pressures are difficult to balance. Here, a new algorithm called Self Organizing Classifiers is proposed which faces this problem from a different perspective. Instead of balancing the pressures, both pressures are separated and no balance is necessary. In fact, the proposed algorithm possesses a dynamical population structure that self-organizes itself to better project the input space into a map. The niched fitness concept is defined along with its dynamical population structure, both are indispensable for the understanding of the proposed method. Promising results are shown on two continuous multi-step problems. One of which is yet more challenging than previous problems of this class in the literature.
UR - http://www.scopus.com/inward/record.url?scp=84883056658&partnerID=8YFLogxK
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U2 - 10.1145/2463372.2463501
DO - 10.1145/2463372.2463501
M3 - Conference contribution
AN - SCOPUS:84883056658
SN - 9781450319638
T3 - GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference
SP - 1109
EP - 1116
BT - GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference
T2 - 2013 15th Genetic and Evolutionary Computation Conference, GECCO 2013
Y2 - 6 July 2013 through 10 July 2013
ER -