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GECCO
2007
Springer
558views Optimization» more  GECCO 2007»
15 years 11 months ago
A chain-model genetic algorithm for Bayesian network structure learning
Bayesian Networks are today used in various fields and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]....
Ratiba Kabli, Frank Herrmann, John McCall
GECCO
2007
Springer
200views Optimization» more  GECCO 2007»
15 years 11 months ago
Performance analysis of niching algorithms based on derandomized-ES variants
A survey of niching algorithms, based on 5 variants of derandomized Evolution Strategies (ES), is introduced. This set of niching algorithms, ranging from the very first derandom...
Ofer M. Shir, Thomas Bäck
GECCO
2007
Springer
174views Optimization» more  GECCO 2007»
15 years 11 months ago
Investigating data-flow coverage of classes using evolutionary algorithms
It is not unusual for a software development organization to expend 40 percent of total project effort on testing, which can be a very laborious and time-consuming process. Thus, ...
Konstantinos Liaskos, Marc Roper, Murray Wood
GECCO
2007
Springer
163views Optimization» more  GECCO 2007»
15 years 11 months ago
Choice and development
The process of development creates a phenotype from one or more genotypes of an individual through interaction with an environment. The opportunity for development to choose a phe...
Arthur M. Farley
GECCO
2007
Springer
164views Optimization» more  GECCO 2007»
15 years 11 months ago
Learning building block structure from crossover failure
In the classical binary genetic algorithm, although crossover within a building block (BB) does not always cause a decrease in fitness, any decrease in fitness results from the ...
Zhenhua Li, Erik D. Goodman
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