Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Hybrib NSGA-II optimization for improving the three-term BP network for multiclass classification problems

Recently, hybrid algorithms have received considerable attention from a number of researchers. This paper presents a hybrid of the multiobjective evolutionary algorithm to gain a better accuracy of the fi nal solutions.The aim of using the hybrid algorithm is to improve the multiobjective evolutionary algorithm performance in terms of the enhancement of all the individuals in the population and increase the quality of the Pareto optimal solutions.The multiobjective evolutionary algorithm used in this study is a nondominated sorting genetic algorithm-II (NSGA-II) together with its hybrid, the backpropagation algorithm (BP), which is used as a local search algorithm to optimize the accuracy and complexity of the three-term backpropagation (TBP) network. The outcome positively demonstrates that the hybrid algorithm is able to improve the classification performance with a smaller number of hidden nodes and is effective in multiclass classifi cation problems.Furthermore, the results indicate that the proposed hybrid method is a potentially useful classifi er for enhancing the classification process ability when compared with the multiobjective genetic algorithm based on the TBP network (MOGATBP) and certain other methods found in the literature

A Hybrid Method for Searching Near-Optimal Artificial Neural Networks

This paper describes a method for searching near-optimal neural networks using Genetic Algorithms. The method uses an evolutionary search with the simultaneous selection of initial weights, transfer functions, architectures and learning rules. Experimental results have shown that the method is able to produce compact, efficient networks with satisfactory generalization power and shorter training times in comparison to other algorithms. 1

Problem Decomposition and Adaptation in Cooperative Neuro-Evolution

One way to train neural networks is to use evolutionary algorithms such as cooperative coevolution - a method that decomposes the network's learnable parameters into subsets, called subcomponents. Cooperative coevolution gains advantage over other methods by evolving particular subcomponents independently from the rest of the network. Its success depends strongly on how the problem decomposition is carried out. This thesis suggests new forms of problem decomposition, based on a novel and intuitive choice of modularity, and examines in detail at what stage and to what extent the different decomposition methods should be used. The new methods are evaluated by training feedforward networks to solve pattern classification tasks, and by training recurrent networks to solve grammatical inference problems. Efficient problem decomposition methods group interacting variables into the same subcomponents. We examine the methods from the literature and provide an analysis of the nature of the neural network optimization problem in terms of interacting variables. We then present a novel problem decomposition method that groups interacting variables and that can be generalized to neural networks with more than a single hidden layer. We then incorporate local search into cooperative neuro-evolution. We present a memetic cooperative coevolution method that takes into account the cost of employing local search across several sub-populations. The optimisation process changes during evolution in terms of diversity and interacting variables. To address this, we examine the adaptation of the problem decomposition method during the evolutionary process. The results in this thesis show that the proposed methods improve performance in terms of optimization time, scalability and robustness. As a further test, we apply the problem decomposition and adaptive cooperative coevolution methods for training recurrent neural networks on chaotic time series problems. The proposed methods show better performance in terms of accuracy and robustness

Simulators: evolutionary multi-agent system for object recognition in satellite image.

Miu, Hoi Shun.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 170-182).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgement --- p.vChapter 1 --- Introduction --- p.1Chapter 1.1 --- Problem Statement --- p.4Chapter 1.2 --- Contributions --- p.5Chapter 1.3 --- Thesis Organization --- p.6Chapter 2 --- Background --- p.8Chapter 2.1 --- Multi-agent Systems --- p.8Chapter 2.1.1 --- Agent Architectures --- p.9Chapter 2.1.2 --- Multi-agent system frameworks --- p.12Chapter 2.1.3 --- The Advantages and Disadvantages of Multi-agent Systems --- p.15Chapter 2.2 --- Evolutionary Computation --- p.16Chapter 2.2.1 --- Genetic Algorithms --- p.17Chapter 2.2.2 --- Genetic Programming --- p.18Chapter 2.2.3 --- Evolutionary Strategies --- p.19Chapter 2.2.4 --- Evolutionary Programming --- p.19Chapter 2.3 --- Object Recognition --- p.19Chapter 2.3.1 --- Knowledge Representation --- p.20Chapter 2.3.2 --- Object Recognition Methods --- p.21Chapter 2.4 --- Evolutionary Multi-agent Systems --- p.25Chapter 2.4.1 --- Competitive Coevolutionary Agents --- p.26Chapter 2.4.2 --- Cooperative Coevolutionary Agents --- p.26Chapter 2.4.3 --- Cellular Automata --- p.27Chapter 2.4.4 --- Emergent Behavior --- p.28Chapter 2.4.5 --- Evolutionary Agents for Image processing and Pattern Recog- nition --- p.29Chapter 3 --- System Architecture and Agent Behaviors in SIMULATORS --- p.33Chapter 3.1 --- Organization of the System --- p.34Chapter 3.1.1 --- General Architecture of Object Recognition System --- p.34Chapter 3.1.2 --- Introduction to SIMULATORS --- p.35Chapter 3.1.3 --- System Flow of SIMULATORS --- p.37Chapter 3.1.4 --- Layered Digital Image Environment --- p.39Chapter 3.2 --- Architecture of Autonomous Agents --- p.41Chapter 3.2.1 --- Internal Object Model in an Agent --- p.41Chapter 3.2.2 --- Current State of an Agent --- p.46Chapter 3.2.3 --- Local Information Sensor --- p.46Chapter 3.2.4 --- Direction Density Vector --- p.47Chapter 3.3 --- Agent Behaviors --- p.48Chapter 3.3.1 --- Feature Target Marking --- p.49Chapter 3.3.2 --- Reproduction --- p.49Chapter 3.3.3 --- Diffusion --- p.52Chapter 3.3.4 --- Vanishing --- p.54Chapter 3.4 --- Clustering for Autonomous Agent Training --- p.56Chapter 3.4.1 --- Introduction --- p.56Chapter 3.4.2 --- Creating the Internal Object Model --- p.58Chapter 3.5 --- Summary --- p.63Chapter 4 --- Evolutionary Algorithms for Multi Agent System --- p.64Chapter 4.1 --- Evolutionary Agent Behaviors in SIMULATORS --- p.65Chapter 4.1.1 --- Overview --- p.65Chapter 4.1.2 --- Evolutionary Autonomous Agents --- p.66Chapter 4.1.3 --- Reproduction --- p.68Chapter 4.1.4 --- Fitness Function --- p.68Chapter 4.1.5 --- Direction Density Vector Propagation --- p.73Chapter 4.1.6 --- Mutation --- p.73Chapter 4.2 --- Agents Voting Mechanism --- p.74Chapter 4.2.1 --- Overview --- p.74Chapter 4.2.2 --- Voting for Cooperative Agents --- p.75Chapter 4.3 --- Evolutionary Multi Agent Object Recognition --- p.79Chapter 4.4 --- Summary --- p.81Chapter 5 --- Experimental Results and Applications --- p.82Chapter 5.1 --- Experiment Methodology --- p.82Chapter 5.1.1 --- Introduction to Fung Shui Woodland --- p.83Chapter 5.1.2 --- Testing Images --- p.83Chapter 5.1.3 --- Creating Internal Object Model --- p.85Chapter 5.1.4 --- Experiment Parameters --- p.86Chapter 5.2 --- Experimental Results of Fung Shui Woodland Recognition --- p.92Chapter 5.2.1 --- Experiment 1: artificial0l --- p.92Chapter 5.2.2 --- Experiment 2: artificial0l´ؤnoise --- p.92Chapter 5.2.3 --- Experiment 3: artificial02 --- p.93Chapter 5.2.4 --- Experiment 4: FungShui0l --- p.93Chapter 5.2.5 --- Experiment 5: FungShui0l´ؤnoise --- p.94Chapter 5.2.6 --- Experiments 6 to 11: FungShui02 to FungShui07 --- p.94Chapter 5.3 --- Discussion --- p.119Chapter 5.4 --- An Example of Eyes Detection --- p.124Chapter 5.4.1 --- Result of the Eyes Detection --- p.128Chapter 5.5 --- Summary --- p.132Chapter 6 --- Conclusion --- p.133Chapter 6.1 --- Summary --- p.133Chapter 6.2 --- Future Work --- p.136Chapter A --- The Figures in the Experiments --- p.13

Recurrent error-based ridge polynomial neural networks for time series forecasting

Time series forecasting has attracted much attention due to its impact on many practical applications. Neural networks (NNs) have been attracting widespread interest as a promising tool for time series forecasting. The majority of NNs employ only autoregressive (AR) inputs (i.e., lagged time series values) when forecasting time series. Moving-average (MA) inputs (i.e., errors) however have not adequately considered. The use of MA inputs, which can be done by feeding back forecasting errors as extra network inputs, alongside AR inputs help to produce more accurate forecasts. Among numerous existing NNs architectures, higher order neural networks (HONNs), which have a single layer of learnable weights, were considered in this research work as they have demonstrated an ability to deal with time series forecasting and have an simple architecture. Based on two HONNs models, namely the feedforward ridge polynomial neural network (RPNN) and the recurrent dynamic ridge polynomial neural network (DRPNN), two recurrent error-based models were proposed. These models were called the ridge polynomial neural network with error feedback (RPNN-EF) and the ridge polynomial neural network with error-output feedbacks (RPNN-EOF). Extensive simulations covering ten time series were performed. Besides RPNN and DRPNN, a pi-sigma neural network and a Jordan pi-sigma neural network were used for comparison. Simulation results showed that introducing error feedback to the models lead to significant forecasting performance improvements. Furthermore, it was found that the proposed models outperformed many state-of-the-art models. It was concluded that the proposed models have the capability to efficiently forecast time series and that practitioners could benefit from using these forecasting models

Deep Learning Techniques for Mobility Prediction and Management in Mobile Networks

Trajectory prediction is an important research topic in modern mobile networks (e.g., 5G and beyond 5G) to enhance the network quality of service by accurately predicting the future locations of mobile users, such as pedestrians and vehicles, based on their past mobility patterns. A trajectory is defined as the sequence of locations the user visits over time. The primary objective of this thesis is to improve the modeling of mobility data and establish personalized, scalable, collective-intelligent, distributed, and strategic trajectory prediction techniques that can effectively adapt to the dynamics of urban environments in order to facilitate the optimal delivery of mobility-aware network services. Our proposed approaches aim to increase the accuracy of trajectory prediction while minimizing communication and computational costs leading to more efficient mobile networks. The thesis begins by introducing a personalized trajectory prediction technique using deep learning and reinforcement learning. It adapts the neural network architecture to capture the distinct characteristics of mobile users’ data. Furthermore, it introduces advanced anticipatory handover management and dynamic service migration techniques that optimize network management using our high-performance trajectory predictor. This approach ensures seamless connectivity and proactively migrates network services, enhancing the quality of service in dense wireless networks. The second contribution of the thesis introduces cluster-level prediction to extend the reinforcement learning-based trajectory prediction, addressing scalability challenges in large-scale networks. Cluster-level trajectory prediction leverages users’ similarities within clusters to train only a few representatives. This enables efficient transfer learning of pre-trained mobility models and reduces computational overhead enhancing the network scalability. The third contribution proposes a collaborative social-aware multi-agent trajectory prediction technique that accounts for the interactions between multiple intra-cluster agents in a dynamic urban environment, increasing the prediction accuracy but decreasing the algorithm complexity and computational resource usage. The fourth contribution proposes a federated learning-driven multi-agent trajectory prediction technique that leverages the collaborative power of multiple local data sources in a decentralized manner to enhance user privacy and improve the accuracy of trajectory prediction while jointly minimizing computational and communication costs. The fifth contribution proposes a game theoretic non-cooperative multi-agent prediction technique that considers the strategic behaviors among competitive inter-cluster mobile users. The proposed approaches are evaluated on small-scale and large-scale location-based mobility datasets, where locations could be GPS coordinates or cellular base station IDs. Our experiments demonstrate that our proposed approaches outperform state-of-the-art trajectory prediction methods making significant contributions to the field of mobile networks