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Found 12 papers, 4 papers with code
Diagonal Acceleration for Covariance Matrix Adaptation Evolution Strategies
In numerical experiments with dd-CMA-ES up to dimension 5120, we observe remarkable improvements over the original covariance matrix adaptation on functions with coordinate-wise ill-conditioning.
Uncrowded Hypervolume Improvement: COMO-CMA-ES and the Sofomore framework
We present a framework to build a multiobjective algorithm from single-objective ones.
COCO: The Large Scale Black-Box Optimization Benchmarking (bbob-largescale) Test Suite
The bbob-largescale test suite, containing 24 single-objective functions in continuous domain, extends the well-known single-objective noiseless bbob test suite, which has been used since 2009 in the BBOB workshop series, to large dimension.
On Bi-Objective convex-quadratic problems
In this paper we analyze theoretical properties of bi-objective convex-quadratic problems.
COCO: Performance Assessment
We present an any-time performance assessment for benchmarking numerical optimization algorithms in a black-box scenario, applied within the COCO benchmarking platform.
Biobjective Performance Assessment with the COCO Platform
This document details the rationales behind assessing the performance of numerical black-box optimizers on multi-objective problems within the COCO platform and in particular on the biobjective test suite bbob-biobj.
The CMA Evolution Strategy: A Tutorial
This tutorial introduces the CMA Evolution Strategy (ES), where CMA stands for Covariance Matrix Adaptation.
Using Well-Understood Single-Objective Functions in Multiobjective Black-Box Optimization Test Suites
Several test function suites are being used for numerical benchmarking of multiobjective optimization algorithms.
COCO: The Experimental Procedure
We present a budget-free experimental setup and procedure for benchmarking numericaloptimization algorithms in a black-box scenario.
COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
We introduce COCO, an open source platform for Comparing Continuous Optimizers in a black-box setting.
Maximum Likelihood-based Online Adaptation of Hyper-parameters in CMA-ES
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is widely accepted as a robust derivative-free continuous optimization algorithm for non-linear and non-convex optimization problems.
Markov Chain Analysis of Evolution Strategies on a Linear Constraint Optimization Problem
This paper analyses a $(1,\lambda)$-Evolution Strategy, a randomised comparison-based adaptive search algorithm, on a simple constraint optimisation problem.
