Artificial neural networks (ANN) have been widely used in classification. They are complicated networks due to the training algorithm used to fix their weights. To achieve better neural network performance, many evolutionary and meta-heuristic algorithms are used to optimize the network weights. The aim of this paper is to implement recently evolutionary algorithms for optimizing neural weights such as Grass Root Optimization (GRO), Artificial Bee Colony (ABC), Cuckoo Search Optimization (CSA) and Practical Swarm Optimization (PSO). This ANN was examined to classify three classes of EEG signals healthy subjects, subjects with interictal epilepsy seizure, and subjects with ictal epilepsy seizures. The above training algorithms are compared according to classification rate, training and testing mean square error, average time, and maximum iteration.

This paper proposes a novel meta-heuristic optimization algorithm inspired by general grass plants fibrous root system, asexual reproduction, and plant development. Grasses search for water and minerals randomly by developing its location, length, primary root, regenerated secondary roots, and small branches of roots called hair roots. The proposed algorithm explore the bounded solution domain globally and locally. Globally using the best grasses survived by the last iteration, and the root system of the best grass obtained so far by the iteration process and locally uses the primary roots, regenerated secondary roots and hair roots of the best global grass. Each grass represents a global candidate solution, while regenerated secondary roots stand for the locally obtained solution. Secondary generated hair roots are equal to the problem dimensions. The performance of the proposed algorithm is tested using seven standard benchmark test functions, comparing it with other meta-heuristic well-known and recently proposed algorithms.

This paper proposes a novel metaheuristic optimization algorithm and suggests an adaptive artificial neural network controller that based on the proposed optimization algorithm. The purpose of the neural controller is to track desired proposed velocities and path trajectory with the minimum error, in the presence of mobile robot parameters time variation and system model uncertainties. The proposed controller consists of two sub-neural controllers; the kinematic neural feedback controller, and the dynamic neural feedback controller. The external feedback kinematic neural controller was responsible of generating the velocity tracking signals that track the mobile robot linear and angular velocities depending on the robot posture error, and the desired velocities. On the other hand, the internal dynamic neural controller has been used to enhance the mobile robot against parameters uncertainty, parameters time variation, and disturbance noise. However, the proposed grass root population-based metaheuristic optimization algorithm has been used to optimize the weights of the neural network to have the behavior of an adaptive nonlinear trajectory tracking controller of a differential drive wheeled mobile robot. The proposed controller shows a very good ability to prepare an appropriate dynamic control left and right torque signals to drive various mobile robot platforms using the same offline optimized weights. Grass root optimization algorithms have been used due to their unique characteristics especially, theirs derivative free, ability to optimize discretely and continuous nonlinear functions, and ability to escape of local minimum solutions.