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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Vydáno v:	2020 IEEE Symposium Series on Computational Intelligence (SSCI) s. 996 - 1003
Hlavní autoři:	Prager, Raphael Patrick, Trautmann, Heike, Wang, Hao, Back, Thomas H. W., Kerschke, Pascal
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 01.12.2020
Témata:	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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Shrnutí:	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 distinct advancements are implemented as modules which result in 4608 unique versions of CMA-ES. Previous findings illustrate the competitive advantage of enabling and disabling the aforementioned modules for different optimization problems. Yet, this modular CMA-ES is lacking a method to automatically determine when the activation of specific modules is auspicious and when it is not. We propose a well-performing instance-specific algorithm configuration model which selects an (almost) optimal configuration of modules for a given problem instance. In addition, the structure of this configuration model is able to capture inter-dependencies between modules, e.g., two (or more) modules might only be advantageous in unison for some problem types, making the orchestration of modules a crucial task. This is accomplished by chaining multiple random forest classifiers together into a so-called Classifier Chain based on a set of numerical features extracted by means of Exploratory Landscape Analysis (ELA) to describe the given problem instances.
DOI:	10.1109/SSCI47803.2020.9308510