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The $(1+(\lambda,\lambda))$ genetic algorithm is one of the few algorithms for which a super-constant speed-up through the use of crossover could be proven.
Drift analysis and average time complexity of evolutionary algorithms
Jun He and Xin Yao · 2001
Earlier work this paper cites.
The analysis of evolutionary algorithms - a proof that crossover really can help
Thomas Jansen and Ingo Wegener · 2002
Earlier work this paper cites.
The Ising model on the ring: Mutation versus recombination
Simon Fischer and Ingo Wegener · 2004
Earlier work this paper cites.
Crossover is provably essential for the Ising model on trees
Dirk Sudholt · 2005
Earlier work this paper cites.
Analyzing randomized search heuristics: Tools from probability theory
Benjamin Doerr · 2011
Earlier work this paper cites.
Runtime analysis of evolutionary algorithms for discrete optimization
Pietro Simone Oliveto and Xin Yao · 2011
Earlier work this paper cites.
Crossover can provably be useful in evolutionary computation
Benjamin Doerr, Edda Happ, and Christian Klein · 2012
Earlier work this paper cites.
Multiplicative drift analysis
Benjamin Doerr, Daniel Johannsen, and Carola Winzen · 2012
Cited alongside, same era.
Black-box search by unbiased variation
Per Kristian Lehre and Carsten Witt · 2012
Cited alongside, same era.
Lessons from the black-box: Fast crossover-based genetic algorithms
Benjamin Doerr, Carola Doerr, and Franziska Ebel · 2013
Cited alongside, same era.
Adaptive drift analysis
Benjamin Doerr and Leslie Ann Goldberg · 2013
Cited alongside, same era.
Analyzing Evolutionary Algorithms—The Computer Science Perspective
Thomas Jansen · 2013
Cited alongside, same era.
Tight bounds on the optimization time of a randomized search heuristic on linear functions
Carsten Witt · 2013
Cited alongside, same era.
Parameter-less population pyramid
Brian W. Goldman and William F. Punch · 2014
Later among the works it cites.
Optimal parameter choices through self-adjustment: Applying the 1/5-th rule in discrete settings
Benjamin Doerr and Carola Doerr · 2015
Later among the works it cites.
A tight runtime analysis of the (1+( λ \lambda , λ \lambda )) genetic algorithm on OneMax
Benjamin Doerr and Carola Doerr · 2015
Later among the works it cites.
From black-box complexity to designing new genetic algorithms
Benjamin Doerr, Carola Doerr, and Franziska Ebel · 2015
Later among the works it cites.
Population size vs. mutation strength for the (1+ λ \lambda ) EA on OneMax
Christian Gießen and Carsten Witt · 2015
Later among the works it cites.
Hard test generation for maximum flow algorithms with the fast crossover-based evolutionary algorithm
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Vladimir Mironovich and Maxim Buzdalov · 2015
Later among the works it cites.