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AEI
2005
99views more  AEI 2005»
14 years 9 months ago
Comparison among five evolutionary-based optimization algorithms
Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural biological evolution and/or the social behavior of species. Such algorithms have been developed ...
Emad Elbeltagi, Tarek Hegazy, Donald E. Grierson
TSE
2010
132views more  TSE 2010»
14 years 4 months ago
ASCENT: An Algorithmic Technique for Designing Hardware and Software in Tandem
Search-based software engineering is an emerging paradigm that uses automated search algorithms to help designers iteratively find solutions to complicated design problems. For exa...
Jules White, Brian Doughtery, Douglas C. Schmidt
HEURISTICS
2008
120views more  HEURISTICS 2008»
14 years 9 months ago
A local linear embedding module for evolutionary computation optimization
A Local Linear Embedding (LLE) module enhances the performance of two Evolutionary Computation (EC) algorithms employed as search tools in global optimization problems. The LLE em...
Fabio Boschetti
GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
15 years 1 months ago
Evolutionary learning with kernels: a generic solution for large margin problems
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
Ingo Mierswa
CEC
2008
IEEE
15 years 4 months ago
New evaluation criteria for the convergence of continuous evolutionary algorithms
—The first hitting time (FHT) plays an important role in convergence evaluation for evolutionary algorithms. However, the current criteria of the FHT are mostly under a hypothesi...
Ying Lin, Jian Huang, Jun Zhang