BBOB
PulseAugur coverage of BBOB — every cluster mentioning BBOB across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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Adaptive Multi Mode optimizer routes tasks dynamically based on landscape probes
Researchers have developed Adaptive Multi Mode (AMM), a novel system designed to dynamically route optimization tasks among various engines based on real-time analysis of the problem's landscape. AMM probes for characte…
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New Adaptive Multi Mode optimizer routes budgets for continuous black-box problems
Researchers have developed Adaptive Multi Mode (AMM), a novel system for optimizing continuous black-box problems. AMM dynamically routes budgets among various optimization engines, including quasi-Newton, trust-region,…
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New DGO Optimizer Analyzes Local Search, Population, and Solver Performance
This paper introduces the Da Gou Gun Optimization (DGO) method, a population optimizer designed to analyze the impact of evaluated candidates on local search steps, population dynamics, and overall solver performance. T…
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Language models learn search strategies via practice, transferable as text
Researchers have developed a method called Code-to-Harness that enables language models to learn numerical search strategies through practice and then distill these strategies into text. This approach significantly redu…
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JANUS module enhances black-box optimization with Jacobian alignment
Researchers have developed JANUS, a novel plug-and-play module designed to enhance population-based optimizers like CMA-ES. JANUS extracts local geometric signals from recent evaluation traces without replacing the host…
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New method uses code complexity to characterize optimization problems
Researchers have developed a new method for characterizing optimization problems by analyzing the complexity of their programmatic representation. This approach uses measures like Halstead volume and code entropy, which…
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New analysis of SOMA and Differential Evolution algorithms published
This paper introduces an operator-selection factorization to analyze the proposal geometry of the Self-Organizing Migrating Algorithm (SOMA) and Differential Evolution (DE). The research demonstrates that the canonical …
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New unsupervised multi-kernel learning method for automated algorithm selection
Researchers have developed a novel unsupervised multi-kernel learning approach for automated algorithm selection in black-box optimization. This method groups problem instances based on heterogeneous landscape represent…
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BayesEvolve framework enhances autonomous scientific discovery with explicit belief states
Researchers have introduced BayesEvolve, a new framework designed to enhance autonomous scientific discovery by incorporating explicit, uncertainty-aware belief states. Unlike systems that rely solely on experimental me…
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CMA-ES stopping criteria performance analyzed
This paper analyzes the effectiveness of 11 different stopping criteria within the CMA-ES black-box optimization algorithm. Researchers quantitatively evaluated these criteria on the BBOB function set, focusing on their…
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Algorithm selection models tested for real-world generalization
Researchers have evaluated the real-world generalizability of algorithm selection models, which aim to automatically pick the best optimization algorithm for a given problem. Their study used both synthetic benchmarks a…