An accurate COG defuzzifier design using Lamarckian co-adaptation of learning and evolution

This paper proposes a design technique of optimal center of gravity (COG) defuzzifier using the Lamarckian co-adaptation of learning and evolution. The proposed COG defuzzifier is specified by various design parameters such as the centers, widths, and modifiers of MFs. The design parameters are adjusted with the Lamarckian co-adaptation of learning and evolution, where the learning performs a local search of design parameters in an individual COG defuzzifier, but the evolution performs a global search of design parameters among a population of various COG defuzzifiers. This co-adaptation scheme allows to evolve much faster than the non-learning case and gives a higher possibility of finding an optimal solution due to its wider searching capability. An application to the truck backer-upper control problem of the proposed co-adaptive design method of COG defuzzifier is presented. The approximation ability and control performance are compared with those of the conventionally simplified COG defuzzifier in terms of the fuzzy logic controller's approximation error and the average tracing distance, respectively.