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Found 47 papers, 10 papers with code
Abnormal Mutations: Evolution Strategies Don't Require Gaussianity
The mutation process in evolution strategies has been interlinked with the normal distribution since its inception.
Transfer Learning of Surrogate Models: Integrating Domain Warping and Affine Transformations
Surrogate models provide efficient alternatives to computationally demanding real-world processes but often require large datasets for effective training.
Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks
Surrogate models are frequently employed as efficient substitutes for the costly execution of real-world processes.
Stalling in Space: Attractor Analysis for any Algorithm
However, STNs are not typically modelled in such a way that models temporal stalls: that is, a region in the search space where an algorithm fails to find a better solution over a defined period of time.
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.
In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristics
Large Language Models (LLMs) have shown great potential in automatically generating and optimizing (meta)heuristics, making them valuable tools in heuristic optimization tasks.
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.
Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks
This study explores the influence of modules on the performance of modular optimization frameworks for continuous single-objective black-box optimization.
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.
Empirical Analysis of the Dynamic Binary Value Problem with IOHprofiler
Optimization problems in dynamic environments have recently been the source of several theoretical studies.
Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization
The recently proposed MA-BBOB function generator provides a way to create numerical black-box benchmark problems based on the well-established BBOB suite.
Using the Empirical Attainment Function for Analyzing Single-objective Black-box Optimization Algorithms
A widely accepted way to assess the performance of iterative black-box optimizers is to analyze their empirical cumulative distribution function (ECDF) of pre-defined quality targets achieved not later than a given runtime.
Large-scale Benchmarking of Metaphor-based Optimization Heuristics
The number of proposed iterative optimization heuristics is growing steadily, and with this growth, there have been many points of discussion within the wider community.
Impact of spatial transformations on landscape features of CEC2022 basic benchmark problems
When benchmarking optimization heuristics, we need to take care to avoid an algorithm exploiting biases in the construction of the used problems.
Explainable Benchmarking for Iterative Optimization Heuristics
Introducing the IOH-Xplainer software framework, for analyzing and understanding the performance of various optimization algorithms and the impact of their different components and hyper-parameters.
MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts
Choosing a set of benchmark problems is often a key component of any empirical evaluation of iterative optimization heuristics.
PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization
Our proposed method creates algorithm behavior meta-representations, constructs a graph from a set of algorithms based on their meta-representation similarity, and applies a graph algorithm to select a final portfolio of diverse, representative, and non-redundant algorithms.
Comparing Algorithm Selection Approaches on Black-Box Optimization Problems
Performance complementarity of solvers available to tackle black-box optimization problems gives rise to the important task of algorithm selection (AS).
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.
MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts
Extending a recent suggestion to generate new instances for numerical black-box optimization benchmarking by interpolating pairs of the well-established BBOB functions from the COmparing COntinuous Optimizers (COCO) platform, we propose in this work a further generalization that allows multiple affine combinations of the original instances and arbitrarily chosen locations of the global optima.
Assessing the Generalizability of a Performance Predictive Model
A key component of automated algorithm selection and configuration, which in most cases are performed using supervised machine learning (ML) methods is a good-performing predictive model.
Challenges of ELA-guided Function Evolution using Genetic Programming
Within the optimization community, the question of how to generate new optimization problems has been gaining traction in recent years.
Analysis of modular CMA-ES on strict box-constrained problems in the SBOX-COST benchmarking suite
Specifically, we want to understand the performance difference between BBOB and SBOX-COST as a function of two initialization methods and six constraint-handling strategies all tested with modular CMA-ES.
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.
Modular Differential Evolution
Although these contributions are often compared to the base algorithm, it is challenging to make fair comparisons between larger sets of algorithm variants.
Deep-BIAS: Detecting Structural Bias using Explainable AI
Recently, the BIAS toolbox was introduced as a behaviour benchmark to detect structural bias (SB) in search algorithms.
Using Affine Combinations of BBOB Problems for Performance Assessment
By analyzing performance trajectories on more function combinations, we also show that aspects such as the scaling of objective functions and placement of the optimum can greatly impact how these results are interpreted.
To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features
These features are then used to predict what algorithm to switch to.
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.
Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms
In this work, we evaluate a performance prediction model built on top of the extension of the recently proposed OPTION ontology.
BBOB Instance Analysis: Landscape Properties and Algorithm Performance across Problem Instances
Benchmarking is a key aspect of research into optimization algorithms, and as such the way in which the most popular benchmark suites are designed implicitly guides some parts of algorithm design.
OPTION: OPTImization Algorithm Benchmarking ONtology
Many optimization algorithm benchmarking platforms allow users to share their experimental data to promote reproducible and reusable research.
Analyzing the Impact of Undersampling on the Benchmarking and Configuration of Evolutionary Algorithms
Particularly, we show that the number of runs used in many benchmarking studies, e. g., the default value of 15 suggested by the COCO environment, can be insufficient to reliably rank algorithms on well-known numerical optimization benchmarks.
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.
The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants
In addition, we have shown that by using classifiers that take the features relevance on the model accuracy, we are able to predict the status of individual modules in the CMA-ES configurations.
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.
Switching between Numerical Black-box Optimization Algorithms with Warm-starting Policies
In this work, we build on the recent study of Vermetten et al. [GECCO 2020], who presented a data-driven approach to investigate promising switches between pairs of algorithms for numerical black-box optimization.
The importance of being constrained: dealing with infeasible solutions in Differential Evolution and beyond
Here, we demonstrate that, at least in algorithms based on Differential Evolution, this choice induces notably different behaviours - in terms of performance, disruptiveness and population diversity.
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.
Is there Anisotropy in Structural Bias?
We find that anisotropy is very rare, and even in cases where it is present, there are clear tests for SB which do not rely on any assumptions of isotropy, so we can safely expand the suite of SB tests to encompass these kinds of deficiencies not found by the original tests.
OPTION: OPTImization Algorithm Benchmarking ONtology
Many platforms for benchmarking optimization algorithms offer users the possibility of sharing their experimental data with the purpose of promoting reproducible and reusable research.
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.
Squirrel: A Switching Hyperparameter Optimizer
In this short note, we describe our submission to the NeurIPS 2020 BBO challenge.
IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics
An R programming interface is provided for users preferring to have a finer control over the implemented functionalities.
Towards Dynamic Algorithm Selection for Numerical Black-Box Optimization: Investigating BBOB as a Use Case
One of the most challenging problems in evolutionary computation is to select from its family of diverse solvers one that performs well on a given problem.
Sequential vs. Integrated Algorithm Selection and Configuration: A Case Study for the Modular CMA-ES
In this work we compare sequential and integrated algorithm selection and configuration approaches for the case of selecting and tuning the best out of 4608 variants of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) tested on the Black Box Optimization Benchmark (BBOB) suite.
Online Selection of CMA-ES Variants
An analysis of module activation indicates which modules are most crucial for the different phases of optimizing each of the 24 benchmark problems.
