65 citations · 151 across the 17 of their papers we have counts for
14 papers · 1 filter
Improving Time and Memory Efficiency of Genetic Algorithms by Storing Populations as Minimum Spanning Trees of Patches
Maxim Buzdalov
In many applications of evolutionary algorithms the computational cost of applying operators and storing populations is comparable to the cost of fitness evaluation. Furthermore, b…
Lazy Parameter Tuning and Control: Choosing All Parameters Randomly From a Power-Law Distribution
Denis Antipov, Maxim Buzdalov, Benjamin Doerr
Most evolutionary algorithms have multiple parameters and their values drastically affect the performance. Due to the often complicated interplay of the parameters, setting these v…
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…
First Steps Towards a Runtime Analysis When Starting With a Good Solution
Denis Antipov, Maxim Buzdalov, Benjamin Doerr
The mathematical runtime analysis of evolutionary algorithms traditionally regards the time an algorithm needs to find a solution of a certain quality when initialized with a rando…
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…
Fixed-Target Runtime Analysis
Maxim Buzdalov, Benjamin Doerr, Carola Doerr +1
Runtime analysis aims at contributing to our understanding of evolutionary algorithms through mathematical analyses of their runtimes. In the context of discrete optimization probl…
Fast Mutation in Crossover-based Algorithms
Denis Antipov, Maxim Buzdalov, Benjamin Doerr
The heavy-tailed mutation operator proposed in Doerr, Le, Makhmara, and Nguyen (GECCO 2017), called \emph{fast mutation} to agree with the previously used language, so far was prov…
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…
Better Fixed-Arity Unbiased Black-Box Algorithms
Nina Bulanova, Maxim Buzdalov
In their GECCO'12 paper, Doerr and Doerr proved that the -ary unbiased black-box complexity of OneMax on bits is for . We propose an alternativ…
Runtime Analysis of the Genetic Algorithm on Random Satisfiable 3-CNF Formulas
Maxim Buzdalov, Benjamin Doerr
The genetic algorithm, first proposed at GECCO 2013, showed a surprisingly good performance on so me optimization problems. The theoretical analysis so far was restrict…
An Asynchronous Implementation of the Limited Memory CMA-ES
Viktor Arkhipov, Maxim Buzdalov, Anatoly Shalyto
We present our asynchronous implementation of the LM-CMA-ES algorithm, which is a modern evolution strategy for solving complex large-scale continuous optimization problems. Our im…