most citedMetaheuristic Design of Feedforward Neural Networks: A Review of Two Decades of Research

604 citations · 698 across the 8 of their papers we have counts for

collaborators

8 papers

cs.NE201712 cited

Predictive modeling of die filling of the pharmaceutical granules using the flexible neural tree

Varun Kumar Ojha, Serena Schiano, Chuan-Yu Wu +2

In this work, a computational intelligence (CI) technique named flexible neural tree (FNT) was developed to predict die filling performance of pharmaceutical granules and to identi…

cs.NE201721 cited

ACO for Continuous Function Optimization: A Performance Analysis

Varun Kumar Ojha, Ajith Abraham, Vaclav Snasel

The performance of the meta-heuristic algorithms often depends on their parameter settings. Appropriate tuning of the underlying parameters can drastically improve the performance…

cs.NE20176 cited

Simultaneous Optimization of Neural Network Weights and Active Nodes using Metaheuristics

Varun Kumar Ojha, Ajith Abraham, Vaclav Snasel

Optimization of neural network (NN) significantly influenced by the transfer function used in its active nodes. It has been observed that the homogeneity in the activation nodes do…

q-bio.QM20178 cited

Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate

Varun Kumar Ojha, Konrad Jackowski, Ajith Abraham +1

Prediction of poly(lactic co glycolic acid) (PLGA) micro- and nanoparticles' dissolution rates plays a significant role in pharmaceutical and medical industries. The prediction of…

cs.AI201718 cited

Multiobjective Programming for Type-2 Hierarchical Fuzzy Inference Trees

Varun Kumar Ojha, Vaclav Snasel, Ajith Abraham

This paper proposes a design of hierarchical fuzzy inference tree (HFIT). An HFIT produces an optimum treelike structure, i.e., a natural hierarchical structure that accommodates s…

cs.NE201726 cited

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Varun Kumar Ojha, Ajith Abraham, Václav Snášel

Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives. We…