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Explainable Benchmarking for Iterative Optimization Heuristics

https://doi.org/10.1145/3716638
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-91100-3_7openalex-snapshot
Towards Explainable Metaheuristics: PCA for Trajectory Mining in Evolutionary Algorithms
inferredcites →https://doi.org/10.1007/978-3-031-14714-2_2openalex-snapshot
Improving Nevergrad’s Algorithm Selection Wizard NGOpt Through Automated Algorithm Configuration
inferredcites →https://doi.org/10.1007/978-3-031-25263-1_3openalex-snapshot
Identifying Properties of Real-World Optimisation Problems Through a Questionnaire
inferredcites →https://doi.org/10.1007/978-3-031-30229-9_17openalex-snapshot
Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms
inferredcites →https://doi.org/10.1007/978-3-031-30229-9_25openalex-snapshot
BBOB Instance Analysis: Landscape Properties and Algorithm Performance Across Problem Instances
inferredcites →https://doi.org/10.1007/978-981-99-3814-8_16openalex-snapshot
Evolutionary Approaches to Explainable Machine Learning
inferredcites →https://doi.org/10.1007/s10489-021-02952-9openalex-snapshot
Multi-granularity competition-cooperation optimization algorithm with adaptive parameter configuration
inferredcites →https://doi.org/10.1007/s13278-017-0480-zopenalex-snapshot
A new approximation method for the Shapley value applied to the WTC 9/11 terrorist attack
inferredcites →https://doi.org/10.1016/j.apenergy.2014.12.020openalex-snapshot
Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution
inferredcites →https://doi.org/10.1016/j.asoc.2011.03.001openalex-snapshot
Impacts of invariance in search: When CMA-ES and PSO face ill-conditioned and non-separable problems
inferredcites →https://doi.org/10.1016/j.asoc.2020.106078openalex-snapshot
An easy-to-use real-world multi-objective optimization problem suite
inferredcites →https://doi.org/10.1016/j.asoc.2021.107492openalex-snapshot
Search trajectory networks: A tool for analysing and visualising the behaviour of metaheuristics
inferredcites →https://doi.org/10.1016/j.cor.2013.11.015openalex-snapshot
Towards objective measures of algorithm performance across instance space
inferredcites →https://doi.org/10.1016/j.ejor.2019.07.073openalex-snapshot
Recent advances in selection hyper-heuristics
inferredcites →https://doi.org/10.1016/j.eswa.2018.10.048openalex-snapshot
Success history applied to expert system for underwater glider path planning using differential evolution
inferredcites →https://doi.org/10.1016/j.eswa.2023.121369openalex-snapshot
Adapted techniques of explainable artificial intelligence for explaining genetic algorithms on the example of job scheduling
inferredcites →https://doi.org/10.1016/j.ins.2014.11.035openalex-snapshot
Structural bias in population-based algorithms
inferredcites →https://doi.org/10.1016/j.ins.2015.05.010openalex-snapshot
Algorithm selection for black-box continuous optimization problems: A survey on methods and challenges
inferredcites →https://doi.org/10.1016/j.swevo.2018.08.013openalex-snapshot
A study on rotation invariance in differential evolution
inferredcites →https://doi.org/10.1038/s42256-019-0138-9openalex-snapshot
From local explanations to global understanding with explainable AI for trees
inferredcites →https://doi.org/10.1080/10556788.2020.1808977openalex-snapshot
COCO: a platform for comparing continuous optimizers in a black-box setting
inferredcites →https://doi.org/10.1109/4235.585893openalex-snapshot
No free lunch theorems for optimization
inferredcites →https://doi.org/10.1109/4235.996017openalex-snapshot
A fast and elitist multiobjective genetic algorithm: NSGA-II
inferredcites →https://doi.org/10.1109/access.2022.3210175openalex-snapshot
A Comparison of Global Sensitivity Analysis Methods for Explainable AI With an Application in Genomic Prediction
inferredcites →https://doi.org/10.1109/cec.2005.1554902openalex-snapshot
A Restart CMA Evolution Strategy With Increasing Population Size
inferredcites →https://doi.org/10.1109/cec.2018.8477769openalex-snapshot
Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems
inferredcites →https://doi.org/10.1109/cec48606.2020.9185620openalex-snapshot
Applying Genetic Programming to Improve Interpretability in Machine Learning Models
inferredcites →https://doi.org/10.1109/cec60901.2024.10611779openalex-snapshot
Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks
inferredcites →https://doi.org/10.1109/ssci47803.2020.9308510openalex-snapshot
Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis
inferredcites →https://doi.org/10.1109/ssci50451.2021.9660124openalex-snapshot
Explainable Landscape-Aware Optimization Performance Prediction
inferredcites →https://doi.org/10.1109/tcyb.2020.2970198openalex-snapshot
Genetic Programming for Evolving a Front of Interpretable Models for Data Visualization
inferredcites →https://doi.org/10.1109/tevc.2011.2182651openalex-snapshot
The Automatic Design of Multiobjective Ant Colony Optimization Algorithms
inferredcites →https://doi.org/10.1109/tevc.2017.2680320openalex-snapshot
Simplify Your Covariance Matrix Adaptation Evolution Strategy
inferredcites →https://doi.org/10.1109/tevc.2021.3102863openalex-snapshot
PSO-X: A Component-Based Framework for the Automatic Design of Particle Swarm Optimization Algorithms
inferredcites →https://doi.org/10.1109/tevc.2022.3189848openalex-snapshot
BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain
inferredcites →https://doi.org/10.1109/tevc.2022.3210897openalex-snapshot
Anytime Performance Assessment in Blackbox Optimization Benchmarking
inferredcites →https://doi.org/10.1109/tevc.2022.3232844openalex-snapshot
OPTION: OPTImization Algorithm Benchmarking ONtology
inferredcites →https://doi.org/10.1109/tevc.2024.3462758openalex-snapshot
Using the Empirical Attainment Function for Analyzing Single-Objective Black-Box Optimization Algorithms
inferredcites →https://doi.org/10.1145/1570256openalex-snapshot
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
inferredcites →https://doi.org/10.1145/1570256.1570333openalex-snapshot
Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed
inferredcites →https://doi.org/10.1145/2001576.2001690openalex-snapshot
Exploratory landscape analysis
inferredcites →https://doi.org/10.1145/2939672.2939778openalex-snapshot
"Why Should I Trust You?"
inferredcites →https://doi.org/10.1145/3205651.3205702openalex-snapshot
Proposal of benchmark problem based on real-world car structure design optimization
inferredcites →https://doi.org/10.1145/3321707.3321726openalex-snapshot
What's inside the black-box?
inferredcites →https://doi.org/10.1145/3377930openalex-snapshot
Proceedings of the 2020 Genetic and Evolutionary Computation Conference
inferredcites →https://doi.org/10.1145/3449639openalex-snapshot
Proceedings of the Genetic and Evolutionary Computation Conference
inferredcites →https://doi.org/10.1145/3449726.3463167openalex-snapshot
Tuning as a means of assessing the benefits of new ideas in interplay with existing algorithmic modules
inferredcites →https://doi.org/10.1145/3449726.3463214openalex-snapshot
Quantifying the impact of boundary constraint handling methods on differential evolution
inferredcites →https://doi.org/10.1145/3449726.3463276openalex-snapshot
Paradiseo: from a modular framework for evolutionary computation to the automated design of metaheuristics
inferredcites →https://doi.org/10.1145/3510426openalex-snapshot
IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics
inferredcites →https://doi.org/10.1145/3512290.3528712openalex-snapshot
Learning the characteristics of engineering optimization problems with applications in automotive crash
inferredcites →https://doi.org/10.1145/3512290.3528832openalex-snapshot
The importance of landscape features for performance prediction of modular CMA-ES variants
inferredcites →https://doi.org/10.1145/3583131.3590417openalex-snapshot
Modular Differential Evolution
inferredcites →https://doi.org/10.1145/3583133.3590551openalex-snapshot
Deep BIAS: Detecting Structural Bias using Explainable AI
inferredcites →https://doi.org/10.1145/3583133.3590609openalex-snapshot
DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis
inferredcites →https://doi.org/10.1145/3583133.3596410openalex-snapshot
Patterns of Convergence and Bound Constraint Violation in Differential Evolution on SBOX-COST Benchmarking Suite
inferredcites →https://doi.org/10.1162/evco_a_00236openalex-snapshot
Automated Algorithm Selection on Continuous Black-Box Problems by Combining Exploratory Landscape Analysis and Machine Learning
inferredcites →https://doi.org/10.1162/evco_a_00325openalex-snapshot
Evolutionary Algorithms for Parameter Optimization—Thirty Years Later
inferredcites →https://doi.org/10.1162/evco_a_00333openalex-snapshot
The Importance of Being Constrained: Dealing with Infeasible Solutions in Differential Evolution and Beyond
inferredcites →https://doi.org/10.1162/evco_a_00356openalex-snapshot
Using Machine Learning Methods to Assess Module Performance Contribution in Modular Optimization Frameworks
inferredcites →https://doi.org/10.1613/jair.1.13676openalex-snapshot
A Survey of Methods for Automated Algorithm Configuration
inferredcites →https://doi.org/10.21105/joss.04721openalex-snapshot
GSAreport: Easy to Use Global Sensitivity Reporting
inferredcites →https://doi.org/10.3390/a14020040openalex-snapshot
A Survey of Advances in Landscape Analysis for Optimisation
inferredcites →https://doi.org/10.4230/oasics.dx.2024.27openalex-snapshot
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
inferredcites →https://doi.org/10.5220/0012179400003595openalex-snapshot
Optimizing CMA-ES with CMA-ES
inferredcites →https://doi.org/10.5281/zenodo.13341950openalex-snapshot
IOH-Xplainer - code, experiments and results

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-031-90065-5_2openalex-snapshot
Into the Black Box: Mining Variable Importance with XAI
inferred← citeshttps://doi.org/10.1007/978-3-031-90065-5_25openalex-snapshot
Controlling the Mutation in Large Language Models for the Efficient Evolution of Algorithms
inferred← citeshttps://doi.org/10.1007/978-3-032-29918-5_19openalex-snapshot
Counterfactual User Guidance for Improving Transparent Hyperparameter Tuning
inferred← citeshttps://doi.org/10.1007/978-981-96-2540-6_5openalex-snapshot
XAI for Benchmarking Black-Box Metaheuristics
inferred← citeshttps://doi.org/10.1007/978-981-96-2540-6_6openalex-snapshot
XAI for Algorithm Configuration and Selection
inferred← citeshttps://doi.org/10.1007/s00158-025-03989-xopenalex-snapshot
Surrogate-based automated hyperparameter optimization for expensive automotive crashworthiness optimization
inferred← citeshttps://doi.org/10.1016/j.cosrev.2025.100764openalex-snapshot
A systematic review of explainability in computational intelligence for optimization
inferred← citeshttps://doi.org/10.1016/j.swevo.2026.102288openalex-snapshot
A survey of features used for representing black-box single-objective continuous optimization
inferred← citeshttps://doi.org/10.1016/j.swevo.2026.102449openalex-snapshot
An interpretability framework for Ant Colony Optimization in continuous domains
inferred← citeshttps://doi.org/10.1109/cec65147.2025.11042974openalex-snapshot
Tracing the Interactions of Modular CMA-ES Configurations Across Problem Landscapes
inferred← citeshttps://doi.org/10.1145/3712255.3734245openalex-snapshot
Explainable Benchmarking for Iterative Optimization Heuristics
inferred← citeshttps://doi.org/10.1145/3712255.3734347openalex-snapshot
BLADE: Benchmark suite for LLM-driven Automated Design and Evolution of iterative optimisation heuristics
inferred← citeshttps://doi.org/10.1145/3844613openalex-snapshot
A Machine-based Decision-maker Toward Benchmarking Interactive Multi-criterion Decision-making Procedures
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