20 datasets found

The dataset used in this paper is the CEC2013 benchmark functions.

The dataset used in the paper is a 2-D environment with a starting region S, a destination region D, and an obstacle region ∆. The environment is described by a starting region...

The CEC-2017 benchmark functions dataset is used to evaluate the performance of optimization algorithms. It contains 30 functions with different characteristics, including...

The dataset used in the paper is a partitioned output dataset with a concave constraint.

The dataset used in this paper is a high-dimensional Dixon-Price function.

The dataset used in this paper is a 30-dimensional Rosenbrock function.

The dataset used for the optimization of an experimental apparatus, consisting of 21 correlated parameters.

The dataset used in this paper is a collection of examples for testing the proposed neural solver for distributed elliptic optimal control problems.

Two-phase optimization for protein folding optimization on a three-dimensional AB off-lattice model

The dataset used in the paper is the copying memory task, which consists in remembering a sequence of letters from some alphabet A = {ak}N k=1.

The dataset used in the paper is a simple test function with a discontinuity at x1 = c, x2 = 0.5. The function is unimodal if b = 0, bimodal if b > 0, and has a discontinuity...

The NL4Opt Generation Dataset consists of 1101 examples, divided into the train, dev, and test splits composed of 713, 99, and 289 examples, respectively. Each example consists...

The dataset used in the paper is a synthetic dataset for ridge regression problem over a network of agents, modeled as a Erdos-Renyi graph with m = 30 nodes and edge probability...

The Beale function is a widely used optimization problem that has a single saddle point at (0, 1).

The dataset used in the paper is a 3-d Gaussian distributed dataset.

The dataset used in the paper is a synthetic dataset generated by the component decoupled model described in Section 3.

The authors propose two algorithms for optimizing for the future in non-stationary MDPs.

The dataset used in the paper is a ride-hailing service problem, where N companies compete to put the most vehicles on the road to attract the most customers.

The gasoline blending scheduling problem is a multiobjective optimization problem that involves resource allocation and operation sequencing to meet a refinery's production...

The Adam dataset is a dataset used for training Adam models.