Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems

Majority of Artificial Neural Network (ANN) implementations in autonomous systems use a fixed/user-prescribed network topology, leading to sub-optimal performance and low portability. The existing neuro-evolution of augmenting topology or NEAT paradigm offers a powerful alternative by allowing the network topology and the connection weights to be simultaneously optimized through an evolutionary process. However, most NEAT implementations allow the consideration of only a single objective. There also persists the question of how to tractably introduce topological diversification that mitigates overfitting to training scenarios. To address these gaps, this paper develops a multi-objective neuro-evolution algorithm. While adopting the basic elements of NEAT, important modifications are made to the selection, speciation, and mutation processes. With the backdrop of small-robot path-planning applications, an experience-gain criterion is derived to encapsulate the amount of diverse local environment encountered by the system. This criterion facilitates the evolution of genes that support exploration, thereby seeking to generalize from a smaller set of mission scenarios than possible with performance maximization alone. The effectiveness of the single-objective (optimizing performance) and the multi-objective (optimizing performance and experience-gain) neuro-evolution approaches are evaluated on two different small-robot cases, with ANNs obtained by the multi-objective optimization observed to provide superior performance in unseen scenarios

Genetic algorithms-based quality of service service selection in cloud computing using multilayer perceptron

There exist many similar services by different service providers available within the cloud environment. When the service offerings are packaged with similar functionalities, service consumers will be having a difficult time in evaluating the most relevant services that fit to their individual requirement. To address this issue, this paper proposes an effective services classification in cloud environment, which will classify the equivalent services based on their quality of service (QoS). The attribute selection method is based genetic algorithms (GA) and is designed to rank the cloud services before the attributes are being fed into a multi-layer perceptron (MLP) classification system. The results have shown a considerably high performance of 98.5%

Analytical framework for analyzing brake squeal noise using assumed-modes approach

Sometimes a loud noise or high pitched squeal occurs when the brakes are applied. It is generated during the braking phase and is characterized by a harmonic spectrum. Brake squeal is induced by self-excited vibrations, consequences of local nonlinearities at the contact interface. Many researchers have examined the problem with experimental, analytical, and computational techniques, but there is still no method to fully annihilate brake squeal. This paper deals with presentation of a new model to analyze the brake squeal behavior. In this paper, a lumped-continuous vibration model is presented for the braking system and nonlinear equations are obtained using the Hamilton’s principle. Then, the linearization of nonlinear equations is done around the equilibrium point of system and linear stability analysis is discussed. Furthermore, the effects of different braking parameters such as friction coefficient, rotational speed, pad stiffness, calipers etc. on the brake squeal noise are investigated

GA-based feature subset selection in a spam/non-spam detection system

Spam has created a significant security problem for computer users everywhere. Spammers take an advantage of defrauds to cover parts of messages that can be used for identification of spam. For instance, a spammer does not need to consume much cost and bandwidth for sending junk mails even more than one hundred emails. On the other hand, from the feature selection perspective, one of the specific problems that decrease accuracy of spam and non-spam emails classification is high data dimensionality. Therefore, the reduction of dimensionality is related to decrease the number of irrelevant features. In this paper, a genetic algorithm (GA) is applied during feature selection in effort to decrease the number of useless features in a collection of high-dimensional email body and subject. Next, a Multi-Layer Perceptron (MLP) is employed to classify features that have been selected by the GA. Using LingSpam benchmark corpora as the dataset, the experimental results showed that a GA feature selector with the MLP classifier does not only decrease the data dimensionality but increase the spam detection rate as compared against other classifiers such as SVM and Naïve Bayes

Analytical framework for analyzing brake squeal noise using assumed-modes approach

Sometimes a loud noise or high pitched squeal occurs when the brakes are applied. It is generated during the braking phase and is characterized by a harmonic spectrum. Brake squeal is induced by self-excited vibrations, consequences of local nonlinearities at the contact interface. Many researchers have examined the problem with experimental, analytical, and computational techniques, but there is still no method to fully annihilate brake squeal. This paper deals with presentation of a new model to analyze the brake squeal behavior. In this paper, a lumped-continuous vibration model is presented for the braking system and nonlinear equations are obtained using the Hamilton’s principle. Then, the linearization of nonlinear equations is done around the equilibrium point of system and linear stability analysis is discussed. Furthermore, the effects of different braking parameters such as friction coefficient, rotational speed, pad stiffness, calipers etc. on the brake squeal noise are investigated