A Feature Composition Problem and a Solution Based on C++ Template Metaprogramming

2006 ◽  
pp. 459-470
Author(s):  
Zoltán Porkoláb ◽  
István Zólyomi
Keyword(s):  
Composition Problem ◽  
Template Metaprogramming

scholarly journals High energy cosmic ray interactions and UHECR composition problem

2019 ◽  
Vol 210 ◽  
pp. 02001
Author(s):  
Sergey Ostapchenko
Keyword(s):  
Experimental Data ◽  
Monte Carlo ◽  
Cosmic Rays ◽  
Cosmic Ray ◽  
High Energy ◽  
Ultra High Energy ◽  
Hadronic Interactions ◽  
High Energy Cosmic Rays ◽  
Cosmic Ray Interactions ◽  
Composition Problem

The differences between contemporary Monte Carlo generators of high energy hadronic interactions are discussed and their impact on the interpretation of experimental data on ultra-high energy cosmic rays (UHECRs) is studied. Key directions for further model improvements are outlined. The prospect for a coherent interpretation of the data in terms of the UHECR composition is investigated.

Applying Evolutionary Many-Objective Optimization Algorithms to the Quality-Driven Web Service Composition Problem

2016 ◽  
pp. 170-194
Author(s):  
Arion de Campos Jr. ◽  
Aurora T. R. Pozo ◽  
Silvia R. Vergilio
Keyword(s):  
Web Services ◽  
Web Service ◽  
Service Composition ◽  
Optimization Algorithms ◽  
Web Service Composition ◽  
Nsga Ii ◽  
Preference Relations ◽  
Multi Objective ◽  
Composition Problem

The Web service composition refers to the aggregation of Web services to meet customers' needs in the construction of complex applications. The selection among a large number of Web services that provide the desired functionalities for the composition is generally driven by QoS (Quality of Service) attributes, and formulated as a constrained multi-objective optimization problem. However, many equally important QoS attributes exist and in this situation the performance of the multi-objective algorithms can be degraded. To deal properly with this problem we investigate in this chapter a solution based in many-objective optimization algorithms. We conduct an empirical analysis to measure the performance of the proposed solution with the following preference relations: Controlling the Dominance Area of Solutions, Maximum Ranking and Average Ranking. These preference relations are implemented with NSGA-II using five objectives. A set of performance measures is used to investigate how these techniques affect convergence and diversity of the search in the WSC context.

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