TY - GEN
T1 - A cognitive system based on fuzzy information processing and multi-objective evolutionary algorithm
AU - Bittermann, Michael S.
AU - Ciftcioglu, Özer
AU - Sariyildiz, I. Sevil
PY - 2009
Y1 - 2009
N2 - A cognitive system is presented, which is based on coupling a multi-objective evolutionary algorithm with a fuzzy information processing system. The aim of the system is to identify optimal solutions for multiple criteria that involve linguistic concepts, and to systematically identify a most suitable solution among the alternatives. The cognitive features are formed by the integration of fuzzy information processing for knowledge representation and evolutionary multi-objective optimization resulting in a decision-making outcome among several equally valid options. Cognition is defined as final decision-making based not exclusively on optimization outcomes but also some higher-order aspects, which do not play role in the pure optimization process. By doing so, the decisions are not merely subject to rationales of the computations but they are the resolutions with the presence of environmental considerations integrated into the computations. The work describes a novel fuzzy system structure serving for this purpose and a novel evolutionary multi-objective optimization strategy for effective Pareto-front formation serving for the goal. The machine cognition is exemplified by means of a design example, where a number of objects are optimally placed according to a number of architectural criteria.
AB - A cognitive system is presented, which is based on coupling a multi-objective evolutionary algorithm with a fuzzy information processing system. The aim of the system is to identify optimal solutions for multiple criteria that involve linguistic concepts, and to systematically identify a most suitable solution among the alternatives. The cognitive features are formed by the integration of fuzzy information processing for knowledge representation and evolutionary multi-objective optimization resulting in a decision-making outcome among several equally valid options. Cognition is defined as final decision-making based not exclusively on optimization outcomes but also some higher-order aspects, which do not play role in the pure optimization process. By doing so, the decisions are not merely subject to rationales of the computations but they are the resolutions with the presence of environmental considerations integrated into the computations. The work describes a novel fuzzy system structure serving for this purpose and a novel evolutionary multi-objective optimization strategy for effective Pareto-front formation serving for the goal. The machine cognition is exemplified by means of a design example, where a number of objects are optimally placed according to a number of architectural criteria.
KW - Cognitive design
KW - Fuzzy neural tree
KW - Multi-objective optimization
KW - Pareto front
KW - Soft computing
UR - https://www.scopus.com/pages/publications/70449816767
U2 - 10.1109/CEC.2009.4983091
DO - 10.1109/CEC.2009.4983091
M3 - Conference contribution
AN - SCOPUS:70449816767
SN - 9781424429592
T3 - 2009 IEEE Congress on Evolutionary Computation, CEC 2009
SP - 1271
EP - 1280
BT - 2009 IEEE Congress on Evolutionary Computation, CEC 2009
T2 - 2009 IEEE Congress on Evolutionary Computation, CEC 2009
Y2 - 18 May 2009 through 21 May 2009
ER -