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GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
14 years 8 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
83
Voted
CEC
2011
IEEE
13 years 11 months ago
Effective ranking + speciation = Many-objective optimization
—Multiobjective optimization problems have been widely addressed using evolutionary computation techniques. However, when dealing with more than three conflicting objectives (th...
Mario Garza-Fabre, Gregorio Toscano Pulido, Carlos...
71
Voted
GECCO
2004
Springer
15 years 4 months ago
Improving Generalisation Performance Through Multiobjective Parsimony Enforcement
This paper describes POPE-GP, a system that makes use of the NSGA-II multiobjective evolutionary algorithm as an alternative, parameter-free technique for eliminating program bloat...
Yaniv Bernstein, Xiaodong Li, Victor Ciesielski, A...
97
Voted
CEC
2010
IEEE
15 years 12 hour ago
Many-objective Distinct Candidates Optimization using Differential Evolution on centrifugal pump design problems
Many-objective problems are difficult to solve using conventional multi-objective evolutionary algorithms (MOEAs) as these algorithms rely primarily on Pareto ranking to guide the ...
Peter Dueholm Justesen, Rasmus K. Ursem
BMCBI
2005
94views more  BMCBI 2005»
14 years 10 months ago
YANA - a software tool for analyzing flux modes, gene-expression and enzyme activities
Background: A number of algorithms for steady state analysis of metabolic networks have been developed over the years. Of these, Elementary Mode Analysis (EMA) has proven especial...
Roland Schwarz, Patrick Musch, Axel von Kamp, Bern...