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

Raphael Patrick Prager, Heike Trautmann, Hao Wang, Thomas H. W. Bäck, Pascal Kerschke

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

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.
Original languageEnglish
Title of host publication IEEE Symposium Series on Computational Intelligence (SSCI)
Pages996-1003
Number of pages8
ISBN (Electronic)978-1-7281-2547-3
DOIs
Publication statusPublished - 5 Jan 2021
Externally publishedYes
EventIEEE Symposium Series on Computational Intelligence, SSCI 2020 - Virtual Event
Duration: 1 Dec 20204 Dec 2020
http://www.ieeessci2020.org/

Conference

ConferenceIEEE Symposium Series on Computational Intelligence, SSCI 2020
Abbreviated titleSSCI 2020
CityVirtual Event
Period1/12/204/12/20
Internet address

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