Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis

In this paper, we rely on previous work proposing a modularized version of CMA-ES, which captures several alterations to the conventional CMA-ES developed in recent years. Each alteration provides significant advantages under certain problem properties, e.g., multi-modality, high conditioning. These...

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Veröffentlicht in:	2020 IEEE Symposium Series on Computational Intelligence (SSCI) S. 996 - 1003
Hauptverfasser:	Prager, Raphael Patrick, Trautmann, Heike, Wang, Hao, Back, Thomas H. W., Kerschke, Pascal
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.12.2020
Schlagworte:	Algorithm Configuration Classifier Chains Context modeling Exploratory Landscape Analysis Machine learning algorithms Modularized CMA-ES Optimization Prediction algorithms Single-Objective Continuous Optimization Sociology Task analysis Transforms
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