Evolutionary algorithms for parameter optimization—thirty years later
Thirty years, 1993–2023, is a huge time frame in science. We address some major
developments in the field of evolutionary algorithms, with applications in parameter …
developments in the field of evolutionary algorithms, with applications in parameter …
Runtime analysis of a heavy-tailed genetic algorithm on jump functions
It was recently observed that the (1+(λ, λ)) genetic algorithm can comparably easily escape
the local optimum of the jump functions benchmark. Consequently, this algorithm can …
the local optimum of the jump functions benchmark. Consequently, this algorithm can …
Automated configuration of genetic algorithms by tuning for anytime performance
Finding the best configuration of algorithms' hyperparameters for a given optimization
problem is an important task in evolutionary computation. We compare in this work the …
problem is an important task in evolutionary computation. We compare in this work the …
General Boolean Function Benchmark Suite
Just over a decade ago, the first comprehensive review on the state of benchmarking in
Genetic Programming (GP) analyzed the mismatch between the problems that are used to …
Genetic Programming (GP) analyzed the mismatch between the problems that are used to …
Fuzzy normalization-based Multi-Attributive Border Approximation Area Comparison
In e-commerce, robust multi-criteria decision analysis is essential for accurately aligning
rankings with customer preferences. Addressing this need, the Fuzzy Normalization-based …
rankings with customer preferences. Addressing this need, the Fuzzy Normalization-based …
The importance of landscape features for performance prediction of modular CMA-ES variants
Selecting the most suitable algorithm and determining its hyperparameters for a given
optimization problem is a challenging task. Accurately predicting how well a certain …
optimization problem is a challenging task. Accurately predicting how well a certain …
Lower bounds for non-elitist evolutionary algorithms via negative multiplicative drift
B Doerr - Evolutionary Computation, 2021 - direct.mit.edu
A decent number of lower bounds for non-elitist population-based evolutionary algorithms
has been shown by now. Most of them are technically demanding due to the (hard to avoid) …
has been shown by now. Most of them are technically demanding due to the (hard to avoid) …
Towards large scale automated algorithm design by integrating modular benchmarking frameworks
We present a first proof-of-concept use-case that demonstrates the efficiency of interfacing
the algorithm framework ParadisEO with the automated algorithm configuration tool irace …
the algorithm framework ParadisEO with the automated algorithm configuration tool irace …
Tight runtime bounds for static unary unbiased evolutionary algorithms on linear functions
In a seminal paper in 2013, Witt showed that the (1+ 1) Evolutionary Algorithm with standard
bit mutation needs time (1+ o (1)) n ln n/p 1 to find the optimum of any linear function, as long …
bit mutation needs time (1+ o (1)) n ln n/p 1 to find the optimum of any linear function, as long …
Leveraging benchmarking data for informed one-shot dynamic algorithm selection
A key challenge in the application of evolutionary algorithms in practice is the selection of an
algorithm instance that best suits the problem at hand. What complicates this decision further …
algorithm instance that best suits the problem at hand. What complicates this decision further …