I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Deep Neural Networks (DNNs) have achieved great success in many applications. The architectures of DNNs play a crucial role in their performance, which is usually manually designed with rich expertise. However, such a design process is labour intensive because of the trial-and-error process, and also not easy to realize due to the rare expertise in practice. Neural Architecture Search (NAS) is a type of technology that can design the architectures automatically. Among different methods to realize NAS, Evolutionary Computation (EC) methods have recently gained much attention and success. Unfortunately, there has not yet been a comprehensive summary of the EC-based NAS algorithms. This paper reviews over 200 papers of most recent EC-based NAS methods in light of the core components, to systematically discuss their design principles as well as justifications on the design. Furthermore, current challenges and issues are also discussed to identify future research in this emerging field.

A Survey on Evolutionary Neural Architecture Search

Deep learning (DL) has become an important machine learning approach that has been widely successful in many applications. Currently, DL is one of the best methods of extracting knowledge from large sets of raw data in a (nearly) self-organized manner. The technical design of DL depends on the feed-forward information flow principle of artificial neural networks with multiple layers of hidden neurons, which form deep neural networks (DNNs). DNNs have various architectures and parameters and are often developed for specific applications. However, the training process of DNNs can be prolonged based on the application and training set size (Gong et al. 2015). Moreover, finding the most accurate and efficient architecture of a deep learning system in a reasonable time is a potential difficulty associated with this approach. Swarm intelligence (SI) and evolutionary computing (EC) techniques represent simulation-driven non-convex optimization frameworks with few assumptions based on objective functions. These methods are flexible and have been proven effective in many applications; therefore, they can be used to improve DL by optimizing the applied learning models. This paper presents a comprehensive survey of the most recent approaches involving the hybridization of SI and EC algorithms for DL, the architecture of DNNs, and DNN training to improve the classification accuracy. The paper reviews the significant roles of SI and EC in optimizing the hyper-parameters and architectures of a DL system in context to large scale data analytics. Finally, we identify some open problems for further research, as well as potential issues related to DL that require improvements, and an extensive bibliography of the pertinent research is presented.

A survey of swarm and evolutionary computing approaches for deep learning

Optimizing long short-term memory recurrent neural networks using ant colony optimization to predict turbine engine vibration

Long short-term memory neural network based fault detection and isolation for electro-mechanical actuators

This paper examines building viable Recurrent Neural Networks (RNN) using Long Short Term Memory (LSTM) neurons to predict aircraft engine vibrations. The model is trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, and this database contains multiple types of engines. Further, LSTM RNNs provide a “memory” of the contribution of previous time series data which can further improve predictions of future vibration values. LSTM RNNs were used over traditional RNNs, as those suffer from vanishing/exploding gradients when trained with back propagation. The study managed to predict vibration values for 5, 10 and 20 seconds in the future, with 3.3%, 5.51% and 10.19% mean absolute error, respectively. These neural networks provide a promising means for the future development of warning systems so that suitable actions can be taken before the occurrence of excess vibration to avoid unfavorable situations during flight.

Using LSTM recurrent neural networks to predict excess vibration events in aircraft engines

This work examines the use of ant colony optimization (ACO) to improve long short-term memory (LSTM) recurrent neural networks (RNNs) by refining their cellular structure. The evolved networks were trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. Results were obtained using MPI (Message Passing Interface) on a high performance computing (HPC) cluster, which evolved 1000 different LSTM cell structures using 208 cores over 5 days. The new evolved LSTM cells showed an improvement in prediction accuracy of 1.37%, reducing the mean prediction error from 6.38% to 5.01% when predicting excessive engine vibrations 10 seconds in the future, while at the same time dramatically reducing the number of trainable weights from 21,170 to 11,650. The ACO optimized LSTM also performed significantly better than traditional Nonlinear Output Error (NOE), Nonlinear AutoRegression with eXogenous (NARX) inputs, and Nonlinear Box-Jenkins (NBJ) models, which only reached error rates of 11.45%, 8.47% and 9.77%, respectively. The ACO algorithm employed could be utilized to optimize LSTM RNNs for any time series data prediction task.

Using ant colony optimization to optimize long short-term memory recurrent neural networks

This paper presents the Evolutionary eXploration of Augmenting LSTM Topologies (EXALT) algorithm and its use in evolving recurrent neural networks (RNNs) for time series data prediction. It introduces a new open data set from a coal-fired power plant, consisting of 10 days of per minute sensor recordings from 12 different burners at the plant. This large scale real world data set involves complex dependencies between sensor parameters and makes for challenging data to predict. EXALT provides interesting new techniques for evolving neural networks, including epigenetic weight initialization, where child neural networks re-use parental weights as a starting point to backpropagation, as well as node-level mutation operations which can improve evolutionary progress. EXALT has been designed with parallel computation in mind to further improve performance. Preliminary results were gathered predicting the Main Flame Intensity data parameter, with EXALT strongly outperforming five traditional neural network architectures on the best, average and worst cases across 10 repeated training runs per test case; and was only slightly behind the best trained Elman recurrent neural networks while being significantly more reliable (i.e., much better average and worst case results). Further, EXALT achieved these results 2 to 10 times faster than the traditional methods, in part due to its scalability, showing strong potential to beat traditional architectures given additional runtime.

Evolving Recurrent Neural Networks for Time Series Data Prediction of Coal Plant Parameters

Weight initialization is critical in being able to successfully train artificial neural networks (ANNs), and even more so for recurrent neural networks (RNNs) which can easily suffer from vanishing and exploding gradients. In neuroevolution, where evolutionary algorithms are applied to neural architecture search, weights typically need to be initialized at three different times: when the initial genomes (ANN architectures) are created, when offspring genomes are generated by crossover, and when new nodes or edges are created during mutation. This work explores the difference between the state-of-the-art Xavier and Kaiming methods, and novel Lamarckian weight inheritance for weight initialization during crossover and mutation operations. These are examined using the Evolutionary eXploration of Augmenting Memory Models (EXAMM) neuroevolution algorithm, which is capable of evolving RNNs with a variety of modern memory cells (e.g., LSTM, GRU, MGU, UGRNN and Delta-RNN cells) as well as recurrent connections with varying time skips through a high performance island based distributed evolutionary algorithm. Results show that with statistical significance, the Lamarckian strategy outperforms both Kaiming and Xavier weight initialization, can speed neuroevolution by requiring less backpropagation epochs to be evaluated per genome, and that the neuroevolutionary process provides further benefits to neural network weight optimization.

AbdElRahman A. ElSaid

Papers

Optimizing long short-term memory recurrent neural networks using ant colony optimization to predict turbine engine vibration

Using LSTM recurrent neural networks to predict excess vibration events in aircraft engines

Using ant colony optimization to optimize long short-term memory recurrent neural networks

Evolving Recurrent Neural Networks for Time Series Data Prediction of Coal Plant Parameters

An Experimental Study of Weight Initialization and Lamarckian Inheritance on Neuroevolution