this paper will introduce a new method to obtain the order weightsof the ordered weighted averaging (owa) operator. we will first show therelation between fuzzy quantifiers and neat owa operators and then offer anew combination of them. fuzzy quantifiers are applied for soft computingin modeling the optimism degree of the decision maker. in using neat operators,the ordering of the inputs is not...

This paper will introduce a new method to obtain the order weightsof the Ordered Weighted Averaging (OWA) operator. We will first show therelation between fuzzy quantifiers and neat OWA operators and then offer anew combination of them. Fuzzy quantifiers are applied for soft computingin modeling the optimism degree of the decision maker. In using neat operators,the ordering of the inputs is not...

NeuroEvolution of Augmenting Topologies (NEAT) is a system for evolving neural network topologies along with weights that has proven highly effective and adaptable solving challenging reinforcement learning tasks. This paper analyses NEAT through the lens Search Trajectory Networks (STNs), recently proposed visual approach to study dynamics evolutionary algorithms. Our goal improve understandin...

Hirsch and Hodkinson proved, for 3 ≤ m < ω and any k < ω, that the class SNrmCAm+k+1 is strictly contained in SNrmCAm+k and if k ≥ 1 then the former class cannot be defined by any finite set of first order formulas, within the latter class. We generalise this result to the following algebras of m-ary relations for which the neat reduct operator Nrm is meaningful: polyadic algebras with or witho...

In this paper, we present Neural Adaptive Tomography (NeAT), the first adaptive, hierarchical neural rendering pipeline for tomography. Through a combination of features with an adaptive explicit representation, achieve reconstruction times far superior to existing inverse methods. The representation improves efficiency by facilitating empty space culling and concentrating samples in complex re...

Most preferred ordered weighted average (MP-OWA) operator is a new kind of neat (dynamic weight) OWA operator in the aggregation operator families. It considers the preferences of all alternatives across the criteria and provides unique aggregation characteristics in decision making. In this paper, we propose the parametric form of the MP-OWA operator to deal with the uncertainty preference inf...