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GECCO
2009
Springer
126views Optimization» more  GECCO 2009»
15 years 3 months ago
Improving NSGA-II with an adaptive mutation operator
The performance of a Multiobjective Evolutionary Algorithm (MOEA) is crucially dependent on the parameter setting of the operators. The most desired control of such parameters pre...
Arthur Gonçalves Carvalho, Aluizio F. R. Ar...
GECCO
2006
Springer
133views Optimization» more  GECCO 2006»
15 years 2 months ago
Selecting for evolvable representations
Evolutionary algorithms tend to produce solutions that are not evolvable: Although current fitness may be high, further search is impeded as the effects of mutation and crossover ...
Joseph Reisinger, Risto Miikkulainen
GECCO
2006
Springer
132views Optimization» more  GECCO 2006»
15 years 2 months ago
Combining genetic algorithms with squeaky-wheel optimization
The AI optimization algorithm called "Squeaky-Wheel Optimization" (SWO) has proven very effective in a variety of real-world applications. Although the ideas behind SWO ...
Justin Terada, Hoa Vo, David Joslin
DOCENG
2008
ACM
15 years 4 days ago
Keeping a digital library clean: new solutions to old problems
Digital Libraries are complex information systems that involve rich sets of digital objects and their respective metadata, along with multiple organizational structures and servic...
Alberto H. F. Laender, Marcos André Gon&cce...
EC
2002
111views ECommerce» more  EC 2002»
14 years 10 months ago
Fitness Landscapes and Evolvability
In this paper, we develop techniques based on evolvability statistics of the fitness landscape surrounding sampled solutions. Averaging the measures over a sample of equal fitness...
Tom Smith, Phil Husbands, Paul J. Layzell, Michael...