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GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
13 years 6 months ago
Uncertainty handling CMA-ES for reinforcement learning
The covariance matrix adaptation evolution strategy (CMAES) has proven to be a powerful method for reinforcement learning (RL). Recently, the CMA-ES has been augmented with an ada...
Verena Heidrich-Meisner, Christian Igel
GECCO
2011
Springer
346views Optimization» more  GECCO 2011»
12 years 11 months ago
Evolving relationships between social networks and stakeholder involvement in software projects
Software projects often fail because stakeholder communication and involvement are inadequate. This paper proposes a novel method to understand project social networks and their c...
Soo Ling Lim, Peter J. Bentley
CEC
2011
IEEE
12 years 8 months ago
Comparing alternative energy functions for the HP model of protein structure prediction
Abstract—Protein structure prediction is the problem of finding the functional conformation of a protein given only its amino uence. The HP lattice model is an abstract formulat...
Mario Garza-Fabre, Eduardo Rodriguez-Tello, Gregor...
GECCO
2007
Springer
154views Optimization» more  GECCO 2007»
14 years 2 months ago
A multi-objective approach to search-based test data generation
There has been a considerable body of work on search–based test data generation for branch coverage. However, hitherto, there has been no work on multi–objective branch covera...
Kiran Lakhotia, Mark Harman, Phil McMinn
GECCO
2005
Springer
109views Optimization» more  GECCO 2005»
14 years 1 months ago
Investigations in meta-GAs: panaceas or pipe dreams?
A meta-GA (GA within a GA) is used to investigate evolving the parameter settings of genetic operators for genetic and evolutionary algorithms (GEA) in the hope of creating a self...
Jeff Clune, Sherri Goings, Bill Punch, Eric Goodma...