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GECCO
2010
Springer
190views Optimization» more  GECCO 2010»
14 years 5 days ago
Comparing the (1+1)-CMA-ES with a mirrored (1+2)-CMA-ES with sequential selection on the noiseless BBOB-2010 testbed
In this paper, we compare the (1+1)-CMA-ES to the (1+2s m)CMA-ES, a recently introduced quasi-random (1+2)-CMAES that uses mirroring as derandomization technique as well as a sequ...
Anne Auger, Dimo Brockhoff, Nikolaus Hansen
GECCO
2010
Springer
196views Optimization» more  GECCO 2010»
14 years 5 days ago
Using synthetic test suites to empirically compare search-based and greedy prioritizers
The increase in the complexity of modern software has led to the commensurate growth in the size and execution time of the test suites for these programs. In order to address this...
Zachary D. Williams, Gregory M. Kapfhammer
GECCO
2006
Springer
208views Optimization» more  GECCO 2006»
13 years 11 months ago
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Both genetic algorithms (GAs) and temporal difference (TD) methods have proven effective at solving reinforcement learning (RL) problems. However, since few rigorous empirical com...
Matthew E. Taylor, Shimon Whiteson, Peter Stone
GECCO
2010
Springer
187views Optimization» more  GECCO 2010»
13 years 9 months ago
Mirrored variants of the (1, 2)-CMA-ES compared on the noiseless BBOB-2010 testbed
Derandomization by means of mirroring has been recently introduced to enhance the performances of (1, λ)-EvolutionStrategies (ESs) with the aim of designing fast robust local sea...
Anne Auger, Dimo Brockhoff, Nikolaus Hansen
GECCO
2010
Springer
157views Optimization» more  GECCO 2010»
13 years 9 months ago
Mirrored variants of the (1, 4)-CMA-ES compared on the noiseless BBOB-2010 testbed
Derandomization by means of mirrored samples has been recently introduced to enhance the performances of (1, λ)Evolution-Strategies (ESs) with the aim of designing fast and robus...
Anne Auger, Dimo Brockhoff, Nikolaus Hansen