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FOGA
2011
12 years 8 months ago
Computational complexity analysis of simple genetic programming on two problems modeling isolated program semantics
Analyzing the computational complexity of evolutionary algorithms (EAs) for binary search spaces has significantly informed our understanding of EAs in general. With this paper, ...
Greg Durrett, Frank Neumann, Una-May O'Reilly
FOGA
1994
13 years 6 months ago
Predictive Models Using Fitness Distributions of Genetic Operators
An important goal of the theory of genetic algorithms is to build predictive models of how well genetic algorithms are expected to perform, given a representation, a fitness lands...
John J. Grefenstette
GECCO
2005
Springer
109views Optimization» more  GECCO 2005»
13 years 11 months ago
Information landscapes and the analysis of search algorithms
In [15] we introduced the information landscape as a new concept of a landscape. We showed that for a landscape of a small size, information landscape theory can be used to predic...
Yossi Borenstein, Riccardo Poli
GECCO
2006
Springer
166views Optimization» more  GECCO 2006»
13 years 9 months ago
Comparing genetic robustness in generational vs. steady state evolutionary algorithms
Previous research has shown that evolutionary systems not only try to develop solutions that satisfy a fitness requirement, but indirectly attempt to develop genetically robust so...
Josh Jones, Terry Soule
GECCO
2007
Springer
532views Optimization» more  GECCO 2007»
13 years 11 months ago
Evolving evolutionary algorithms using evolutionary algorithms
A new model for automatic generation of Evolutionary Algorithms (EAs) by evolutionary means is proposed in this paper. The model is based on a simple Genetic Algorithm (GA). Every...
Laura Diosan, Mihai Oltean