17 datasets found

The dataset used to test the proposed new mutation and evaluation function.

The Maximin Latin Hypercube Designs dataset is used to test the proposed new mutation and evaluation function.

The dataset used in the paper is the Traveling Salesman Problem (TSP) instances, specifically the Burma'7 instance, and six additional instances with 5 to 14 nodes.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used various optimization algorithms to solve complex problems.

The dataset used in the paper is the MATPOWER dataset.

The benchmark test-suite is a collection of twelve well-known test functions from the literature, including unimodal, multimodal, separable, and non-separable functions. The...

The (cid:96)1 ball constrained optimization problem.

The dataset used in the paper is a collection of 30 test functions with 30 and 50 dimensions, used for evaluating the performance of the Adaptive Coordinate System (ACoS)...

DaCeML is a Data-Centric Machine Learning framework that provides a simple, flexible, and customizable pipeline for optimizing training of arbitrary deep neural networks.

This dataset is used to test the robustness of watermarking methods against adaptive attacks.

The dataset used in the paper is not explicitly described, but it is mentioned that it is a class of regression models GN.

The CEC2022 basic benchmark problems dataset, which includes 5 problems from the CEC2022 benchmark suite.

The Branin function is a well-known function for benchmarking optimization methods. It is a 2D function with three global maxima.

A synthetic dataset created for the purpose of training a generative model for optimal geometry generation.

Deep Learning (DL) models are widely used in machine learning due to their performance and ability to deal with large datasets while producing high accuracy and performance...

The dataset used in this paper is the Cost Function Library benchmark.

The dataset used in the paper is not explicitly mentioned, but it is mentioned that the authors used Rosenbrock function for optimization.