In the dueling bandit setting, the (1+1) EA selects the Condorcet winner with only constant probability when its advantage is Ω(1/n), while a Max-Min Ant System EDA selects it with probability 1-Θ(p), and repeated duels improve the EA's performance.
Working principles of binary differential evolution
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Quantum-chemical bonding descriptors improve machine learning predictions of materials properties and enable symbolic regression to recover intuitive expressions for force constants and thermal conductivity.
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Analysis of Search Heuristics in the Multi-Armed Bandit Setting
In the dueling bandit setting, the (1+1) EA selects the Condorcet winner with only constant probability when its advantage is Ω(1/n), while a Max-Min Ant System EDA selects it with probability 1-Θ(p), and repeated duels improve the EA's performance.
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A critical assessment of bonding descriptors for predicting materials properties
Quantum-chemical bonding descriptors improve machine learning predictions of materials properties and enable symbolic regression to recover intuitive expressions for force constants and thermal conductivity.