Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning

Abstract

In this paper, we build upon previous work on designing informative and efficient Exploratory Landscape Analysis features for characterizing problems' landscapes and show their effectiveness in automatically constructing algorithm selection models in continuous black-box optimization problems. Focussing on algorithm performance results of the COCO platform of several years, we construct a representative set of high-performing complementary solvers and present an algorithm selection model that - compared to the portfolio's single best solver - on average requires less than half of the resources for solving a given problem. Therefore, there is a huge gain in efficiency compared to classical ensemble methods combined with an increased insight into problem characteristics and algorithm properties by using informative features. Acting on the assumption that the function set of the Black-Box Optimization Benchmark is representative enough for practical applications the model allows for selecting the best suited optimization algorithm within the considered set for unseen problems prior to the optimization itself based on a small sample of function evaluations. Note that such a sample can even be reused for the initial population of an evolutionary (optimization) algorithm so that even the feature costs become negligible.

This is the author's final version, and the article has been accepted for publication in Evolutionary Computation

References in corpus (1)

• Evolving the Structure of Evolution Strategies

Cited by in corpus (27)

• A new Taxonomy of Continuous Global Optimization Algorithms
• Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants
• Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions
• CAAI -- A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical Production Systems
• Incremental Recursive Ranking Grouping for Large Scale Global Optimization
• Initial Design Strategies and their Effects on Sequential Model-Based Optimization
• The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection
• Benchmarking Feature-based Algorithm Selection Systems for Black-box Numerical Optimization
• An Instance Space Analysis of Constrained Multi-Objective Optimization Problems
• Towards Dynamic Algorithm Selection for Numerical Black-Box Optimization: Investigating BBOB as a Use Case
• Analyzing variational quantum landscapes with information content
• Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy
• Personalizing Performance Regression Models to Black-Box Optimization Problems
• Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games
• Towards Exploratory Landscape Analysis for Large-scale Optimization: A Dimensionality Reduction Framework
• Computational Rational Engineering and Development: Synergies and Opportunities
• Benchmarking Parameter Control Methods in Differential Evolution for Mixed-Integer Black-Box Optimization
• Towards Feature-free TSP Solver Selection: A Deep Learning Approach
• Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems
• One-Shot Decision-Making with and without Surrogates
• Does Dimensionality Reduction via Random Projections Preserve Landscape Features?
• A Case Study on Optimization of Platooning Coordination
• On Constructing Algorithm Portfolios in Algorithm Selection for Computationally Expensive Black-box Optimization in the Fixed-budget Setting
• Black Box Algorithm Selection by Convolutional Neural Network
• Optimization is Not Enough: Why Problem Formulation Deserves Equal Attention
• Benchmarking the Hooke-Jeeves Method, MTS-LS1, and BSrr on the Large-scale BBOB Function Set
• Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem