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GECCO
2006
Springer
130views Optimization» more  GECCO 2006»
15 years 10 months ago
Pairwise sequence comparison for fitness evaluation in evolutionary structural software testing
Evolutionary algorithms are among the metaheuristic search methods that have been applied to the structural test data generation problem. Fitness evaluation methods play an import...
H. Turgut Uyar, A. Sima Etaner-Uyar, A. Emre Harma...
GECCO
2006
Springer
133views Optimization» more  GECCO 2006»
15 years 10 months ago
On-line evolutionary computation for reinforcement learning in stochastic domains
In reinforcement learning, an agent interacting with its environment strives to learn a policy that specifies, for each state it may encounter, what action to take. Evolutionary c...
Shimon Whiteson, Peter Stone
GECCO
2006
Springer
181views Optimization» more  GECCO 2006»
15 years 10 months ago
Robustness in cooperative coevolution
Though recent analysis of traditional cooperative coevolutionary algorithms (CCEAs) casts doubt on their suitability for static optimization tasks, our experience is that the algo...
R. Paul Wiegand, Mitchell A. Potter
GECCO
2006
Springer
123views Optimization» more  GECCO 2006»
15 years 10 months ago
Fluctuating crosstalk, deterministic noise, and GA scalability
This paper extends previous work showing how fluctuating crosstalk in a deterministic fitness function introduces noise into genetic algorithms. In that work, we modeled fluctuati...
Paul Winward, David E. Goldberg
GECCO
2006
Springer
161views Optimization» more  GECCO 2006»
15 years 10 months ago
The LEM3 implementation of learnable evolution model and its testing on complex function optimization problems
1 Learnable Evolution Model (LEM) is a form of non-Darwinian evolutionary computation that employs machine learning to guide evolutionary processes. Its main novelty are new type o...
Janusz Wojtusiak, Ryszard S. Michalski
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