No confident code implementation found for this paper. 5 possible matches can be shown below.
Benchmarks are used for testing new optimization algorithms and their variants to evaluate their performance. Most existing benchmarks are smooth functions. This chapter introduces ten new benchmarks with different properties, including noise, discontinuity, parameter estimation and unknown paths.
No confident code match yet
We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 5 weaker matches are hidden by default — verify before relying on them.
No code implementations found yet.
Know of an implementation? Let us know in the comments below!
Evolving Benchmark Functions to Compare Evolutionary Algorithms via Genetic Programming
HRA: A Multi-Criteria Framework for Ranking Metaheuristic Optimization Algorithms
Explainable Benchmarking for Iterative Optimization Heuristics
Several Performance Bounds on Decentralized Online Optimization are Highly Conservative and Potentially Misleading
A Review of 315 Benchmark and Test Functions for Machine Learning Optimization Algorithms and Metaheuristics with Mathematical and Visual Descriptions
Benchmark for CEC 2024 Competition on Multiparty Multiobjective Optimization
@article{yang2023benchmarks,
title = {Ten New Benchmarks for Optimization},
author = {Xin-She Yang},
year = {2023},
eprint = {2309.00644},
archivePrefix = {arXiv},
url = {http://arxiv.org/abs/2309.00644v1}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/2309.00644)