17 citations · 24 across the 5 of their papers we have counts for
8 papers
Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms
Kirill Antonov, Maxim Buzdalov, Arina Buzdalova +1
With the goal to provide absolute lower bounds for the best possible running times that can be achieved by -type search heuristics on common benchmark problems, we recently…
Optimal Static Mutation Strength Distributions for the Evolutionary Algorithm on OneMax
Maxim Buzdalov, Carola Doerr
Most evolutionary algorithms have parameters, which allow a great flexibility in controlling their behavior and adapting them to new problems. To achieve the best performance, it i…
Optimal Mutation Rates for the EA on OneMax
Maxim Buzdalov, Carola Doerr
The OneMax problem, alternatively known as the Hamming distance problem, is often referred to as the "drosophila of evolutionary computation (EC)", because of its high relevance in…
The Genetic Algorithm for Permutations
Anton Bassin, Maxim Buzdalov
The genetic algorithm is a bright example of an evolutionary algorithm which was developed based on the insights from theoretical findings. This algorithm uses crossove…
The 1/5-th Rule with Rollbacks: On Self-Adjustment of the Population Size in the GA
Anton Bassin, Maxim Buzdalov
Self-adjustment of parameters can significantly improve the performance of evolutionary algorithms. A notable example is the genetic algorithm, where the adaptation of…
Black-Box Complexity of the Binary Value Function
Nina Bulanova, Maxim Buzdalov
The binary value function, or BinVal, has appeared in several studies in theory of evolutionary computation as one of the extreme examples of linear pseudo-Boolean functions. Its u…