Automated algorithm selection on continuous black-box problems by combining exploratory landscape analysis and machine learning
In this article, we build upon previous work on designing informative and efficient
Exploratory Landscape Analysis features for characterizing problems' landscapes and show …
Exploratory Landscape Analysis features for characterizing problems' landscapes and show …
Evolutionary algorithms for parameter optimization—thirty years later
Thirty years, 1993–2023, is a huge time frame in science. We address some major
developments in the field of evolutionary algorithms, with applications in parameter …
developments in the field of evolutionary algorithms, with applications in parameter …
Llamea: A large language model evolutionary algorithm for automatically generating metaheuristics
Large Language Models (LLMs) such as GPT-4 have demonstrated their ability to
understand natural language and generate complex code snippets. This paper introduces a …
understand natural language and generate complex code snippets. This paper introduces a …
Per-run algorithm selection with warm-starting using trajectory-based features
Per-instance algorithm selection seeks to recommend, for a given problem instance and a
given performance criterion, one or several suitable algorithms that are expected to perform …
given performance criterion, one or several suitable algorithms that are expected to perform …
Benchmarking discrete optimization heuristics with IOHprofiler
Automated benchmarking environments aim to support researchers in understanding how
different algorithms perform on different types of optimization problems. Such comparisons …
different algorithms perform on different types of optimization problems. Such comparisons …
IOHanalyzer: Detailed performance analyses for iterative optimization heuristics
Benchmarking and performance analysis play an important role in understanding the
behaviour of iterative optimization heuristics (IOHs) such as local search algorithms, genetic …
behaviour of iterative optimization heuristics (IOHs) such as local search algorithms, genetic …
Tuning as a means of assessing the benefits of new ideas in interplay with existing algorithmic modules
Introducing new algorithmic ideas is a key part of the continuous improvement of existing
optimization algorithms. However, when introducing a new component into an existing …
optimization algorithms. However, when introducing a new component into an existing …
Modular differential evolution
New contributions in the field of iterative optimisation heuristics are often made in an
iterative manner. Novel algorithmic ideas are not proposed in isolation, but usually as …
iterative manner. Novel algorithmic ideas are not proposed in isolation, but usually as …
Landscape-aware fixed-budget performance regression and algorithm selection for modular CMA-ES variants
Automated algorithm selection promises to support the user in the decisive task of selecting
a most suitable algorithm for a given problem. A common component of these machine …
a most suitable algorithm for a given problem. A common component of these machine …
Learning step-size adaptation in CMA-ES
An algorithm's parameter setting often affects its ability to solve a given problem, eg,
population-size, mutation-rate or crossover-rate of an evolutionary algorithm. Furthermore …
population-size, mutation-rate or crossover-rate of an evolutionary algorithm. Furthermore …