8 citations · 10 across the 4 of their papers we have counts for
4 papers
Representation-agnostic distance-driven perturbation for optimizing ill-conditioned problems
Kirill Antonov, Anna V. Kononova, Thomas Bäck +1
Locality is a crucial property for efficiently optimising black-box problems with randomized search heuristics. However, in practical applications, it is not likely to always find…
High Dimensional Bayesian Optimization with Kernel Principal Component Analysis
Kirill Antonov, Elena Raponi, Hao Wang +1
Bayesian Optimization (BO) is a surrogate-based global optimization strategy that relies on a Gaussian Process regression (GPR) model to approximate the objective function and an a…
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…
Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation Rates
Anna Rodionova, Kirill Antonov, Arina Buzdalova +1
We analyze the performance of the 2-rate Evolutionary Algorithm (EA) with self-adjusting mutation rate control, its 3-rate counterpart, and a ~EA variant using multi…