Fetching the paper…
Reading the bibliography…
We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms in discrete search spaces.
A remark on Stirling’s formula
Herbert Robbins · 1955
Earlier work this paper cites.
Mean, median and mode in binomial distributions
Rob Kaas and Jan M. Buhrman · 1980
Earlier work this paper cites.
Parameter control in evolutionary algorithms
Agoston Endre Eiben, Robert Hinterding, and Zbigniew Michalewicz · 1999
Earlier work this paper cites.
Evolutionary algorithms and the maximum matching problem
Oliver Giel and Ingo Wegener · 2003
Earlier work this paper cites.
On the choice of the offspring population size in evolutionary algorithms
Thomas Jansen, Kenneth A. De Jong, and Ingo Wegener · 2005
Earlier work this paper cites.
Simulated annealing beats Metropolis in combinatorial optimization
Ingo Wegener · 2005
Earlier work this paper cites.
On the analysis of a dynamic evolutionary algorithm
Thomas Jansen and Ingo Wegener · 2006
Earlier work this paper cites.
Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
Frank Neumann and Ingo Wegener · 2007
Earlier work this paper cites.
Rank based variation operators for genetic algorithms
Jorge Cervantes and Christopher R. Stephens · 2008
Earlier work this paper cites.
Rigorous runtime analysis of inversely fitness proportional mutation rates
Christine Zarges · 2008
Earlier work this paper cites.
Theoretical analysis of local search strategies to optimize network communication subject to preserving the total number of links
Boris Mitavskiy, Jonathan E. Rowe, and Chris Cannings · 2009
Earlier work this paper cites.
Theoretical analysis of rank-based mutation - combining exploration and exploitation
Pietro Simone Oliveto, Per Kristian Lehre, and Frank Neumann · 2009
Earlier work this paper cites.
On the utility of the population size for inversely fitness proportional mutation rates
Christine Zarges · 2009
Cited alongside, same era.
Optimal fixed and adaptive mutation rates for the LeadingOnes problem
Süntje Böttcher, Benjamin Doerr, and Frank Neumann · 2010
Cited alongside, same era.
Tight bounds for blind search on the integers and the reals
Martin Dietzfelbinger, Jonathan E. Rowe, Ingo Wegener, and Philipp Woelfel · 2010
Cited alongside, same era.
Random combinatorial structures and randomized search heuristics
Daniel Johannsen · 2010
Cited alongside, same era.
Non-uniform mutation rates for problems with unknown solution lengths
Stephan Cathabard, Per Kristian Lehre, and Xin Yao · 2011
Cited alongside, same era.
Analyzing randomized search heuristics: tools from probability theory
Benjamin Doerr · 2011
Concentrated hitting times of randomized search heuristics with variable drift
Per Kristian Lehre and Carsten Witt · 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.
Optimizing linear functions with the (1+ λ \lambda ) evolutionary algorithm – different asymptotic runtimes for different instances
Benjamin Doerr and Marvin Künnemann · 2015
Later among the works it cites.
(1+1) EA on generalized dynamic OneMax
Timo Kötzing, Andrei Lissovoi, and Carsten Witt · 2015
Later among the works it cites.
Self-adaptation of mutation rates in non-elitist populations
Duc-Cuong Dang and Per Kristian Lehre · 2016
Later among the works it cites.
Optimal parameter settings for the ( 1 + ( λ , λ ) ) (1+(\lambda,\lambda)) genetic algorithm
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Adaptive population models for offspring populations and parallel evolutionary algorithms
Jörg Lässig and Dirk Sudholt · 2011
Cited alongside, same era.
Multiplicative drift analysis
Benjamin Doerr, Daniel Johannsen, and Carola Winzen · 2012
Cited alongside, same era.
A runtime analysis of simple hyper-heuristics: To mix or not to mix operators
Per Kristian Lehre and Ender Özcan · 2013
Cited alongside, same era.
Runtime analysis of selection hyper-heuristics with classical learning mechanisms
Fawaz Alanazi and Per Kristian Lehre · 2014
Cited alongside, same era.
Unbiased black-box complexity of parallel search
Golnaz Badkobeh, Per Kristian Lehre, and Dirk Sudholt · 2014
Cited alongside, same era.
From black-box complexity to designing new genetic algorithms
Benjamin Doerr, Carola Doerr, and Franziska Ebel
Cited in the paper.
Benjamin Doerr · 2016
Later among the works it cites.
Selection hyper-heuristics can provably be helpful in evolutionary multi-objective optimization
Chao Qian, Ke Tang, and Zhi-Hua Zhou · 2016
Later among the works it cites.
Runtime analysis of the ( 1 + ( λ , λ ) ) (1+(\lambda,\lambda)) genetic algorithm on random satisfiable 3-CNF formulas
Maxim Buzdalov and Benjamin Doerr · 2017
Closest in time.
The interplay of population size and mutation probability in the (1+ λ \lambda ) EA on OneMax
Christian Gießen and Carsten Witt · 2017
Closest in time.
On the runtime analysis of generalised selection hyper-heuristics for pseudo-Boolean optimisation
Andrei Lissovoi, Pietro S. Oliveto, and John Alasdair Warwicker · 2017
Closest in time.
An elementary analysis of the probability that a binomial random variable exceeds its expectation
Benjamin Doerr · 2018
Closest in time.