518 research outputs found
Multi Agent Diagnosis: an analysis
The paper analyzes the use of a Multi Agent System for Model Based Diagnosis. In a large dynamical system, it is often infeasible or even impossible to maintain a model of the whole system. Instead, several incomplete models of the system have to be used to detect possible faults. These models may also be physically be distributed. A Multi Agent System of diagnostic agents may offer solutions for establishing a global diagnosis. If we use a separate agent for each incomplete model of the system, establishing a global diagnosis becomes a problem cooperation and negotiation between the diagnostic agents. This raises the question whether `a set of diagnostic agents, each having an incomplete model of the system, can (efficiently) determine the same global diagnosis as an ideal single diagnostic agent having the combined knowledge of the diagnostic agents?''economics of technology ;
Temporal Decoupling and Determining Resource Needs of Autonomous Agents in the Airport Turnaround Process
Feature Selection of Post-Graduation Income of College Students in the United States
This study investigated the most important attributes of the 6-year
post-graduation income of college graduates who used financial aid during their
time at college in the United States. The latest data released by the United
States Department of Education was used. Specifically, 1,429 cohorts of
graduates from three years (2001, 2003, and 2005) were included in the data
analysis. Three attribute selection methods, including filter methods, forward
selection, and Genetic Algorithm, were applied to the attribute selection from
30 relevant attributes. Five groups of machine learning algorithms were applied
to the dataset for classification using the best selected attribute subsets.
Based on our findings, we discuss the role of neighborhood professional degree
attainment, parental income, SAT scores, and family college education in
post-graduation incomes and the implications for social stratification.Comment: 14 pages, 6 tables, 3 figure
Exploiting linkage information in real-valued optimization with the real-valued gene-pool optimal mixing evolutionary algorithm
The recently introduced Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) has been shown to be among the state-of-the-art for solving discrete optimization problems. Key to the success of GOMEA is its ability to efficiently exploit the linkage structure of a problem. Here, we introduce the Real-Valued GOMEA (RV-GOMEA), which incorporates several aspects of the real-valued EDA known as AMaLGaM into GOMEA in order to make GOMEA well-suited for real-valued optimization. The key strength of GOMEA to competently exploit linkage structure is effectively preserved in RV-GOMEA, enabling excellent performance on problems that exhibit a linkage structure that is to some degree decomposable. Moreover, the main variation operator of GOMEA enables substantial improvements in performance if the problem allows for partial evaluations, which may be very well possible in many real-world applications. Comparisons of performance with state-of-the-art algorithms such as CMA-ES and AMaLGaM on a set of well-known benchmark problems show that RV-GOMEA achieves comparable, excellent scalability in case of black-box optimization. Moreover, RV-GOMEA achieves unprecedented scalability on problems that allow for partial evaluations, reaching near-optimal solutions for problems with up to millions of real-valued variables within one hour on a normal desktop computer
162 (PB-075) Poster - Who is at risk of developing breast cancer-related fatigue:A prediction study
161 (PB-074) Poster - Overview of patient preference sensitive attributes in eHealth interventions for breast cancer-related fatigue
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