Triangular norm-based iterative compensatory operators

TL;DR: The experimental results obtained confirm that the generation of multiple descendents along with the offspring selection mechanism that chooses the two best offspring may enhance the operation of these three crossover operators, and are more efficient than standard real-coded genetic algorithms, that is, they offer solutions with higher quality, requiring fewer fitness function evaluations.

TL;DR: This two-part state-of-the-art overview on aggregation theory summarizes the essential information concerning aggregation issues and gives an overview of aggregation properties, including the basic classification on aggregation functions.

TL;DR: An overview of generated triangular norms and their applications is presented, and several properties of generated $t$-norms are investigated by means of the corresponding generators, including convergence properties.

TL;DR: A modified sampling scheme for use with eNERF is proposed that combines simple random sampling with (parts of) the sampling procedures used by enerF and a related algorithm sVAT (scalable visual assessment of clustering tendency).

TL;DR: The results of the experiments support the hypothesis that people often use compensatory procedures and suggest a new class of operators which varies with respect to a parameter of compensation.

TL;DR: Several types of aggregation operators on fuzzy sets are discussed, all of them based on triangular norms and triangular conorms, and the pointwise aggregation and the crisp output can be obtained afterwards by any defuzzification procedure.

TL;DR: From a mathematical point of view, the use of triangular norms as connectives in many-valued logics and fuzzy logics is discussed, and an overview of some non-additive generalizations of classical measures is given.