The phrase "an algorithm for exploration" is correct and usable in written English.
It can be used in contexts related to computer science, data analysis, or research where a method or procedure is being described for the purpose of exploring data or environments.
Example: "The researchers developed an algorithm for exploration that allowed them to analyze vast amounts of geographical data efficiently."
Alternatives: "a method for exploration" or "a procedure for exploration".
Splotch is a rendering algorithm for exploration and visual discovery in particle-based datasets coming from astronomical observations or numerical simulations.
The classical adversarial algorithm Exp3 (exponential-weight algorithm for exploration and exploitation) is used for task scheduling [6].
In order to find the regions in the parameter space where the constraints (14- 17) are satisfied we use the algorithm for exploration of parameter spaces by Zamora-Sillero et al. [ 22].
In this paper, we introduce an algorithm for discovering mutated driver patterns de novo using somatic mutation data from biological datasets, which is based on recent exploration made by Vandin et al. [ 19] and Zhao et al. [ 21].
We have designed a genetic algorithm for the exploration of the large design space formed by the execution traces.
The approach consists of a discrete event simulator for performance estimation, and an optimization algorithm for design space exploration that integrates a population-based search algorithm with Optimal Computing Budget Allocation OCBAA).
Therefore, the check in the algorithm for further exploration of an intermediate molecule m is based on whether IDbond(m) has been seen before, but the vertices of the resulting ({{mathrm{HoR}}}) hypergraph are based on the ID m) values.
Here, the general strategy is to use one algorithm for global exploration of the search space and another for refinement of local optima, possibly iterating between the global and the local stages.
A novel algorithm for automated simultaneous exploration of datapath and Unrolling Factor (UF) during power performance tradeoff in High Level Synthesis (HLS) using multi-dimensional particle swarm optimization (PSO) (termed as 'M-PSO') for control and data flow graphs (CDFGs) is presented.
