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GECCO
2009
Springer
144views Optimization» more  GECCO 2009»
14 years 3 months ago
Cheating for problem solving: a genetic algorithm with social interactions
We propose a variation of the standard genetic algorithm that incorporates social interaction between the individuals in the population. Our goal is to understand the evolutionary...
Rafael Lahoz-Beltra, Gabriela Ochoa, Uwe Aickelin
SYNTHESE
2008
84views more  SYNTHESE 2008»
13 years 8 months ago
How experimental algorithmics can benefit from Mayo's extensions to Neyman-Pearson theory of testing
Although theoretical results for several algorithms in many application domains were presented during the last decades, not all algorithms can be analyzed fully theoretically. Exp...
Thomas Bartz-Beielstein
GECCO
2003
Springer
14 years 2 months ago
Scalability of Selectorecombinative Genetic Algorithms for Problems with Tight Linkage
Ensuring building-block (BB) mixing is critical to the success of genetic and evolutionary algorithms. This study develops facetwise models to predict the BB mixing time and the po...
Kumara Sastry, David E. Goldberg
GECCO
2005
Springer
112views Optimization» more  GECCO 2005»
14 years 2 months ago
A multiple objective evolutionary algorithm for multiple sequence alignment
The problem of multiple sequence alignment is important for bioinformatics. This problem is widely studied and a popular tool to solve this problem is Clustal X. This work introdu...
Pasut Seeluangsawat, Prabhas Chongstitvatana
ML
2000
ACM
244views Machine Learning» more  ML 2000»
13 years 8 months ago
Learnable Evolution Model: Evolutionary Processes Guided by Machine Learning
A new class of evolutionary computation processes is presented, called Learnable Evolution Model or LEM. In contrast to Darwinian-type evolution that relies on mutation, recombinat...
Ryszard S. Michalski