Search Results for author: Maxim Buzdalov

Found 16 papers, 1 papers with code

Improving Time and Memory Efficiency of Genetic Algorithms by Storing Populations as Minimum Spanning Trees of Patches

1 code implementation29 Jun 2023 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.

Evolutionary Algorithms

Using Automated Algorithm Configuration for Parameter Control

no code implementations23 Feb 2023 Deyao Chen, Maxim Buzdalov, Carola Doerr, Nguyen Dang

Dynamic Algorithm Configuration (DAC) tackles the question of how to automatically learn policies to control parameters of algorithms in a data-driven fashion.

Lazy Parameter Tuning and Control: Choosing All Parameters Randomly From a Power-Law Distribution

no code implementations14 Apr 2021 Denis Antipov, Maxim Buzdalov, Benjamin Doerr

On the other hand, this algorithm is also very efficient on jump functions, where the best static parameters are very different from those necessary to optimize simple problems.

Evolutionary Algorithms

Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms

no code implementations23 Feb 2021 Kirill Antonov, Maxim Buzdalov, Arina Buzdalova, Carola Doerr

With the goal to provide absolute lower bounds for the best possible running times that can be achieved by $(1+\lambda)$-type search heuristics on common benchmark problems, we recently suggested a dynamic programming approach that computes optimal expected running times and the regret values inferred when deviating from the optimal parameter choice.

Evolutionary Algorithms

Optimal Static Mutation Strength Distributions for the $(1+λ)$ Evolutionary Algorithm on OneMax

no code implementations9 Feb 2021 Maxim Buzdalov, Carola Doerr

However, only little is known so far about the influence of these distributions on the performance of evolutionary algorithms, and about the relationships between (dynamic) parameter control and (static) parameter sampling.

Evolutionary Algorithms

First Steps Towards a Runtime Analysis When Starting With a Good Solution

no code implementations22 Jun 2020 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 random population.

Evolutionary Algorithms

Optimal Mutation Rates for the $(1+λ)$ EA on OneMax

no code implementations20 Jun 2020 Maxim Buzdalov, Carola Doerr

With this in hand, we compute for all population sizes $\lambda \in \{2^i \mid 0 \le i \le 18\}$ and for problem dimension $n \in \{1000, 2000, 5000\}$ which mutation rates minimize the expected running time and which ones maximize the expected progress.

Fixed-Target Runtime Analysis

no code implementations20 Apr 2020 Maxim Buzdalov, Benjamin Doerr, Carola Doerr, Dmitry Vinokurov

In this work, we conduct an in-depth study on the advantages and the limitations of fixed-target analyses.

Evolutionary Algorithms

The $(1+(λ,λ))$ Genetic Algorithm for Permutations

no code implementations18 Apr 2020 Anton Bassin, Maxim Buzdalov

The $(1+(\lambda,\lambda))$ genetic algorithm is a bright example of an evolutionary algorithm which was developed based on the insights from theoretical findings.

Evolutionary Algorithms

Fast Mutation in Crossover-based Algorithms

no code implementations14 Apr 2020 Denis Antipov, Maxim Buzdalov, Benjamin Doerr

In this first runtime analysis of a crossover-based algorithm using a heavy-tailed choice of the mutation rate, we show an even stronger impact.

The 1/5-th Rule with Rollbacks: On Self-Adjustment of the Population Size in the $(1+(λ,λ))$ GA

no code implementations15 Apr 2019 Anton Bassin, Maxim Buzdalov

In particular, the one fifth rule, which guides the adaptation in the example above, is able to raise the population size too fast on problems which are too far away from the perfect fitness-distance correlation.

Evolutionary Algorithms

Black-Box Complexity of the Binary Value Function

no code implementations9 Apr 2019 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.

Better Fixed-Arity Unbiased Black-Box Algorithms

no code implementations15 Apr 2018 Nina Bulanova, Maxim Buzdalov

In their GECCO'12 paper, Doerr and Doerr proved that the $k$-ary unbiased black-box complexity of OneMax on $n$ bits is $O(n/k)$ for $2\le k\le O(\log n)$.

Runtime Analysis of the $(1+(λ,λ))$ Genetic Algorithm on Random Satisfiable 3-CNF Formulas

no code implementations14 Apr 2017 Maxim Buzdalov, Benjamin Doerr

We show that this problem can be overcome by equipping the self-adjusting GA with an upper limit for the population size.

Evolutionary Algorithms

An Asynchronous Implementation of the Limited Memory CMA-ES

no code implementations1 Oct 2015 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.

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