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CEC
2007
IEEE
15 years 8 months ago
SAT-decoding in evolutionary algorithms for discrete constrained optimization problems
— For complex optimization problems, several population-based heuristics like Multi-Objective Evolutionary Algorithms have been developed. These algorithms are aiming to deliver ...
Martin Lukasiewycz, Michael Glaß, Christian ...
GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
15 years 6 months ago
On the appropriateness of evolutionary rule learning algorithms for malware detection
In this paper, we evaluate the performance of ten well-known evolutionary and non-evolutionary rule learning algorithms. The comparative study is performed on a real-world classi...
M. Zubair Shafiq, S. Momina Tabish, Muddassar Faro...
IPL
2002
65views more  IPL 2002»
15 years 1 months ago
Parallel evolutionary algorithms can achieve super-linear performance
One of the main reasons for using parallel evolutionary algorithms (PEAs) is to obtain efficient algorithms with an execution time much lower than that of their sequential counter...
Enrique Alba
GECCO
2004
Springer
119views Optimization» more  GECCO 2004»
15 years 7 months ago
Randomized Local Search, Evolutionary Algorithms, and the Minimum Spanning Tree Problem
Randomized search heuristics, among them randomized local search and evolutionary algorithms, are applied to problems whose structure is not well understood, as well as to problems...
Frank Neumann, Ingo Wegener
PPSN
2004
Springer
15 years 7 months ago
An Evolutionary Algorithm for the Maximum Weight Trace Formulation of the Multiple Sequence Alignment Problem
Abstract. The multiple sequence alignment problem (MSA) can be reformulated as the problem of finding a maximum weight trace in an alignment graph, which is derived from all pairw...
Gabriele Koller, Günther R. Raidl