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A genetic algorithm based optimized convolutional neural network for face recognition

Karlupia Namrata, Mahajan Palak, Abrol Pawanesh, Lehana Parveen K.

International Journal of Applied Mathematics and Computer Science

2023

Vol. 33, no. 1

21--31

Face recognition (FR) is one of the most active research areas in the field of computer vision. Convolutional neural networks (CNNs) have been extensively used in this field due to their good efficiency. Thus, it is important to find the best CNN parameters for its best performance. Hyperparameter optimization is one of the various techniques for increasing the performance of CNN models. Since manual tuning of hyperparameters is a tedious and time-consuming task, population based metaheuristic techniques can be used for the automatic hyperparameter optimization of CNNs. Automatic tuning of parameters reduces manual efforts and improves the efficiency of the CNN model. In the proposed work, genetic algorithm (GA) based hyperparameter optimization of CNNs is applied for face recognition. GAs are used for the optimization of various hyperparameters like filter size as well as the number of filters and of hidden layers. For analysis, a benchmark dataset for FR with ninety subjects is used. The experimental results indicate that the proposed GA-CNN model generates an improved model accuracy in comparison with existing CNN models. In each iteration, the GA minimizes the objective function by selecting the best combination set of CNN hyperparameters. An improved accuracy of 94.5% is obtained for FR.

LCS Approach to Tasks Scheduling Problem in the Two Processor System

Wasielewska K., Seredyński F.

Studia Informatica : systems and information technology

2007

Vol. 2(9)

29--39

In this paper we propose an approach to solve multiprocessor scheduling problem with use of rule-based learning machine - Learning Classifier System (LCS). LCS combines reinforcement learning and evolutionary computing to produce adaptive systems. We interpret the multiprocessor scheduling problem as multi-step problem, where a feedback is given after some number steps. We show that LCS is able to solve scheduling tasks of a parallel program in the two processor system.