Paper Info
Title
The EvA2 Optimization Framework
Abstract
We present EvA2, a comprehensive metaheuristic optimization framework with emphasis on Evolutionary Algorithms. It presents a modular structure of interfaces and abstract classes for the implementation of both optimization problems and solvers. End users may choose among several layers of abstraction for an entrance point meeting their requirements on ease of use and access to extensive functionality. The EvA2 framework has been applied successfully in several academic as well as industrial cooperations and is extended continuously. It is freely available under an open source license (LGPL).
Year
DOI
Venue
2010
10.1007/978-3-642-13800-3_27
learning and intelligent optimization
Keywords
Field
DocType
comprehensive metaheuristic optimization framework,abstract class,entrance point,industrial cooperation,end user,optimization problem,modular structure,evolutionary algorithms,eva2 optimization framework,extensive functionality,eva2 framework,evolutionary algorithm,ease of use
End user,Evolutionary algorithm,Computer science,Artificial intelligence,Optimization problem,Modular structure,Distributed computing,License,Mathematical optimization,Metaheuristic optimization,Usability,Abstraction layer,Machine learning
Conference
Volume
ISSN
ISBN
6073
0302-9743
3-642-13799-7
Citations 
PageRank 
References 
28
1.25
3
Authors
3
Name
Order
Citations
PageRank
Marcel Kronfeld1746.67
Hannes Planatscher2765.90
Andreas Zell31419137.58