Algorithm selection for black-box continuous optimization problems

TL;DR: In this paper, an algorithm selection model for continuous black-box optimization problems is presented, based on the assumption that the function set of the Black-Box Optimization Benchmark is representative enough for practical applications.

TL;DR: The solving process is decomposed into four key steps of mathematical modeling of the problem, defining the objective functions, development of an approach and obtaining the solution of theproblem.

TL;DR: Flacco is introduced, an R-package for feature-based landscape analysis of continuous and constrained optimization problems that offers easy access to an essential ingredient of the ASP by providing a wide collection of ELA features on a single platform—even within a single package.

TL;DR: This paper presents a literature survey of classification algorithm recommendation methods, shed light on the motivational reasons for pursuing classifier selection through meta-learning and comprehensively discusses the different phases of classifiers selection based on a generic framework that is formed as an outcome of reviewing prior works.

TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.

TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.

TL;DR: This paper shows empirically and theoretically that randomly chosen trials are more efficient for hyper-parameter optimization than trials on a grid, and shows that random search is a natural baseline against which to judge progress in the development of adaptive (sequential) hyper- parameter optimization algorithms.

TL;DR: A detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far are presented.