a School of Geographical Sciences and Urban Planning, Arizona State University, P.O. Box 875302, Tempe, AZ 85287-5302, United States b Global Institute of Sustainability, Arizona State University, PO Box 875402, Tempe, AZ 85287, United States c Department of Geography, Geology, and Anthropology, Indiana State University, Terre Haute, IN 47809, United States d Potsdam-Institute for Climate Impac...

A key difficulty in Cooperative Coevolutionary Algorithms (CCEAs) is the credit assignment problem[1]. One solution to the credit assignment problem is the difference evaluation function, which produces excellent results in many multiagent domains. However, to date, there has been no prescriptive theoretical analysis deriving conditions under which difference evaluations improve the probability...

The most widely used measure of segregation is the dissimilarity index, D. It is now well understood that this measure also reflects randomness in the allocation of individuals to units; that is, it measures deviations from evenness not deviations from randomness. This leads to potentially large values of the segregation index when unit sizes and/or minority proportions are small, even if there...

probabilistic approach of traffic assignment has been primarily developed to provide a more realistic and flexible theoretical framework to represent traveler’s route choice behavior in a transportation network. the problem of path overlapping in network modelling has been one of the main issues to be tackled. due to its flexible covariance structure, probit model can adequately address the pro...

One of the basic combinatorial optimization problems is the assignment problem that deals with assigning jobs to individuals. In traditional assignment problems, n jobs usually assign to n individuals such that the total cost is minimized or the total profit is maximized. However, in numerous real-life applications, various attributes could be considered in assignment problems while data (obj...

In this paper, we present a method for blind separation of co-channel BPSK signals arriving at an antenna array. This method consists of two parts: the maximum likelihood constellation estimation and assignment. We show that at high SNR, the maximum likelihood constellation estimation is well approximated by the smallest distance clustering algorithm, which we proposed earlier on heuristic grou...

This paper investigates the use of Gaussian Selection (GS) to reduce the state likelihood computation in HMM-based systems. These likelihood calculations contribute significantly (30 to 70%) to the computational load. Previously, it has been reported that when GS is used on large systems the recognition accuracy tends to degrade above a 3 reduction in likelihood computation. To explain this deg...