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» Genetic Algorithms for Dynamic Test Data Generation
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TEC
2008
98views more  TEC 2008»
15 years 2 months ago
Opposition-Based Differential Evolution
Evolutionary Algorithms (EAs) are well-known optimization approaches to cope with non-linear, complex problems. These population-based algorithms, however, suffer from a general we...
Shahryar Rahnamayan, Hamid R. Tizhoosh, Magdy M. A...
NN
2007
Springer
267views Neural Networks» more  NN 2007»
15 years 1 months ago
Modeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization
In the last decade, recurrent neural networks (RNNs) have attracted more efforts in inferring genetic regulatory networks (GRNs), using time series gene expression data from micro...
Rui Xu, Ganesh K. Venayagamoorthy, Donald C. Wunsc...
143
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FLAIRS
2008
15 years 4 months ago
Using Genetic Programming to Increase Rule Quality
Rule extraction is a technique aimed at transforming highly accurate opaque models like neural networks into comprehensible models without losing accuracy. G-REX is a rule extract...
Rikard König, Ulf Johansson, Lars Niklasson
GECCO
2008
Springer
139views Optimization» more  GECCO 2008»
15 years 3 months ago
Evolutionary design of dynamic SwarmScapes
This paper discusses interactive evolutionary algorithms and their application in swarm-based image generation. From an artist’s perspective, the computer-generated patterns oļ¬...
Namrata Khemka, Scott Novakowski, Gerald Hushlak, ...
NDSS
2008
IEEE
15 years 8 months ago
Automated Whitebox Fuzz Testing
Fuzz testing is an effective technique for finding security vulnerabilities in software. Traditionally, fuzz testing tools apply random mutations to well-formed inputs of a progr...
Patrice Godefroid, Michael Y. Levin, David A. Moln...