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Found 15 papers, 4 papers with code
MO-IOHinspector: Anytime Benchmarking of Multi-Objective Algorithms using IOHprofiler
Benchmarking is one of the key ways in which we can gain insight into the strengths and weaknesses of optimization algorithms.
Sampling in CMA-ES: Low Numbers of Low Discrepancy Points
For lower dimensionalities (below 10), we find that using as little as 32 unique low discrepancy points performs similar or better than uniform sampling.
Evolving Reliable Differentiating Constraints for the Chance-constrained Maximum Coverage Problem
Our experiments show that our approach is highly successful in solving the instability issue of the performance ratios and leads to evolving reliable sets of chance constraints with significantly different performance for various types of algorithms.
Avoiding Redundant Restarts in Multimodal Global Optimization
Na\"ive restarts of global optimization solvers when operating on multimodal search landscapes may resemble the Coupon's Collector Problem, with a potential to waste significant function evaluations budget on revisiting the same basins of attractions.
Solving Deep Reinforcement Learning Tasks with Evolution Strategies and Linear Policy Networks
We benchmark both deep policy networks and networks consisting of a single linear layer from observations to actions for three gradient-based methods, such as Proximal Policy Optimization.
Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms
In this work we compare 8 popular numerical black-box optimization algorithms on the $L_{\infty}$ star discrepancy computation problem, using a wide set of instances in dimensions 2 to 15.
When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems
In this work, we use the notion of the resolution of continuous variables to discretize problems from the continuous domain.
Towards Self-adaptive Mutation in Evolutionary Multi-Objective Algorithms
Parameter control has succeeded in accelerating the convergence process of evolutionary algorithms.
Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler
Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns.
Optimizing Stimulus Energy for Cochlear Implants with a Machine Learning Model of the Auditory Nerve
In the second part of this paper, the Convolutional Neural Network surrogate model was used by an Evolutionary Algorithm to optimize the shape of the stimulus waveform in terms energy efficiency.
Per-run Algorithm Selection with Warm-starting using Trajectory-based Features
In contrast to other recent work on online per-run algorithm selection, we warm-start the second optimizer using information accumulated during the first optimization phase.
Trajectory-based Algorithm Selection with Warm-starting
We study the quality and accuracy of performance regression and algorithm selection models in the scenario of predicting different algorithm performances after a fixed budget of function evaluations.
IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics
IOHexperimenter can be used as a stand-alone tool or as part of a benchmarking pipeline that uses other components of IOHprofiler such as IOHanalyzer, the module for interactive performance analysis and visualization.
Explorative Data Analysis of Time Series based AlgorithmFeatures of CMA-ES Variants
From our analysis, we saw that the features can classify the CMA-ES variants, or the function groups decently, and show a potential for predicting the performance of those variants.
Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules
However, when introducing a new component into an existing algorithm, assessing its potential benefits is a challenging task.
