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This paper focuses on developing a computationally economic lightweight artificial intelligence (AI) technology for smartphones. Until date, no commercial system is available on this technology. Thus the developed breakthrough technology can enhance the capability of users on the field for monitoring the agricultural vehicles (AgV)s health by analyzing the acoustic noise using smartphone's app. This paper can enable the user of AgVs to optimize their farming by management at edge devices: smartphones. Since smartphones use a small integrated computing unit with computational limited resources, thus lighter the system, more favorable to work on. Artificial neural network (ANN) is one of the most favorite AI techniques, but its lightweight architecture—attributed by the number of inputs and hidden layers and neurons—, is one of the most important issues in the context of smartphones. Under the framework of bi-level optimization, we aim to analyze the tournament selection operator based genetic algorithm with hybrid crossover operators, at level-I, to evolve ANN, at level-II to design the lightweight edge device enabled AI technique. The obtained results and numerical evaluative analysis indicate that the optimized design of the lightweight fault detection system responds well to the soundtracks received via microphones. We present the evaluation of the proposed technology on serial programming, parallel programming (PP), GPU programming, and GPU with PP. The results show that PP is enough efficient for the proposed technology and can save the cost of GPU for large scale implementation of the technology. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent approach, called HERON (hybrid evolutionary optimization for nutraceutical manufacturing), is proposed to optimize a variety of manufacturing processes in the nutraceutical field. The approach integrates the Taguchi method, an artificial neural network (ANN), and a genetic algorithm (GA). The Taguchi method is used to cost-effectively gather the data on the process parameters. Data obtained by the Taguchi method are divided into input and output data for an ANN's input and output parameters, respectively. The ANN trains itself to develop the relationship between its input and output parameters. The trained ANN is then integrated into a GA as the fitness function, such that the GA can evolutionarily obtain the optimal process parameters. The HERON is validated through a manufacturing process on soft-shell turtle soft-capsules. The objective is to minimize the soft-capsule defect rate. Compared to the defect rates obtained by the empirical and Taguchi methods, the HERON reduces the defect rate by 43.75 and 32.5 %, respectively. In addition, compared to the manufacturing costs obtained by the empirical and Taguchi methods, the HERON reduces the manufacturing cost by 11.81 and 25.29 %, respectively. [ABSTRACT FROM AUTHOR]

Cognitive radios are expected to play a major role towards meeting the exploding traffic demand over wireless systems. A cognitive radio node senses the environment, analyzes the outdoor parameters, and then makes decisions for dynamic time-frequency-space resource allocation and management to improve the utilization of the radio spectrum. For efficient real-time process, the cognitive radio is usually combined with artificial intelligence and machine-learning techniques so that an adaptive and intelligent allocation is achieved. This paper firstly presents the cognitive radio networks, resources, objectives, constraints, and challenges. Then, it introduces artificial intelligence and machine-learning techniques and emphasizes the role of learning in cognitive radios. Then, a survey on the state-of-the-art of machine-learning techniques in cognitive radios is presented. The literature survey is organized based on different artificial intelligence techniques such as fuzzy logic, genetic algorithms, neural networks, game theory, reinforcement learning, support vector machine, case-based reasoning, entropy, Bayesian, Markov model, multi-agent systems, and artificial bee colony algorithm. This paper also discusses the cognitive radio implementation and the learning challenges foreseen in cognitive radio applications. [ABSTRACT FROM AUTHOR]

This paper presents LS-VisionDraughts: an efficient unsupervised evolutionary learning system for Checkers whose contribution is to automate the process of selecting an appropriate representation for the board states - by means of Evolutionary Computation - keeping a deep look-ahead (search depth) at the moment of choosing an adequate move. It corresponds to a player Multi Layer Perceptron Neural Network whose weights are updated through an evaluation function that is automatically adjusted by means of the Temporal Difference methods. A Genetic Algorithm automatically chooses a concise and efficient set of functions, which describe various scenarios associated with Checkers - called features - to represent the board states in the input layer of the Neural Network. It means that each individual of the Genetic Algorithm is a candidate set of features that is associated to a distinct Multi Layer Perceptron Neural Network. The output layer of the Neural Network is a real number ( prediction) that indicates to which extent the input state is favorable to provide a better agent performance. In LS-VisionDraughts, a particular version of the search algorithm Alpha-Beta, called fail-soft Alpha-Beta, combined with Table Transposition, Iterative Deepening and ordered tree, uses this prediction value to choose the best move corresponding to the current board state. The best individual is chosen by means of numerous tournaments involving these selfsame Neural Networks. The architecture of LS-VisionDraught is inspired on the agent NeuroDraughts. However, the former system enhances the performance of the latter by automating the selection of the features through Evolutionary Computation and by replacing its Minimax search algorithm with the improved search strategy resumed above. This procedure allows for a 95 % reduction in the search runtime. Further, it remarkably increases the search tree depth. The results obtained from evaluative tournaments confirm the advances of LS-VisionDraughts compared to its opponents. It is however important to point out that LS-VisionDraughts learns practically without human supervision, contrary to the current automatic world champion Chinook, which has been built in a strongly supervised manner. [ABSTRACT FROM AUTHOR]

The objective of this paper is to find a sequence of jobs in the flow shop to minimize makespan. A feed forward back propagation neural network is used to solve the problem. The network is trained with the optimal sequences of completely enumerated five, six and seven jobs, ten machine problem and this trained network is then used to solve the problem with greater number of jobs. The sequence obtained using artificial neural network (ANN) is given as the initial sequence to a heuristic proposed by Suliman and also to genetic algorithm (GA) as one of the sequences of the population for further improvement. The approaches are referred as ANN-Suliman heuristic and ANN-GA heuristic respectively. Makespan of the sequences obtained by these heuristics are compared with the makespan of the sequences obtained using the heuristic proposed by Nawaz, Enscore and Ham (NEH) and Suliman Heuristic initialized with Campbell Dudek and Smith (CDS) heuristic called as CDS-Suliman approach. It is found that the ANN-GA and ANN-Suliman heuristic approaches perform better than NEH and CDS-Suliman heuristics for the problems considered. [ABSTRACT FROM AUTHOR]

The work presented in this paper is an investigation of the prediction of amplitudes of the specific grinding force components. An improved method for artificial neural networks (ANNs) establishment is proposed here allowing accurate prediction of specific normal and tangential grinding forces. This method can determine the optimal set of inputs to be used for these ANN. This set of inputs is composed of significant factors and interactions among factors that could possibility offer the best learning and generalization of ANNs simultaneously. A 48-run experimental design (MED) is used in this research to train the ANNs and a total of 81 experiments are conducted to test the generalization performances of ANNs. Results have indicated that the developed ANNs show low deviations from the training data, and acceptable deviations from the testing data. In addition, the accuracies of these ANNs are found to be significantly better than those of other approaches used for modelling of the specific grinding force components. These approaches use regression models, power models, genetic algorithms or the common ANNs for which only factors of the MED are usually used in the input layer. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, sensor readings are gathered at a gateway for processing and forwarding to a remote command center. The potential closeness of the gateway to dangerous events, e.g. fires, exposes it to damage and thus risks making the network dysfunctional. Therefore, protecting the gateway by repositioning it away from safety- hazardous spots is critical for the operation of the network. However, moving the gateway too far from the sensors that report on active events would have negative effect on the network performance, e.g. throughput and energy consumption. Therefore, balancing the gateway safety and network performance goals will be necessary. In this paper, we present GRISP, a novel Gateway Relocation algorithm for Improved Safety and Performance. GRISP employs an evolutionary neural network model to assess the safety of the gateway at the various locations. The model is then used to direct the search in an area of interest for a safer position that would enhance or at least maintain an acceptable level of network performance. In addition, GRISP guides the gateway during the move by finding safe paths leading to the new location. Our experimental validation results demonstrate the effectiveness of GRISP. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of a neural network (NN) scheduler for scheduling job-shops. In this hybrid intelligent system, genetic algorithms (GA) are used to generate optimal schedules to a known benchmark problem. In each optimal solution, every individually scheduled operation of a job is treated as a decision which contains knowledge. Each decision is modeled as a function of a set of job characteristics (e.g., processing time), which are divided into classes using domain knowledge from common dispatching rules (e.g., shortest processing time). A NN is used to capture the predictive knowledge regarding the assignment of operation’s position in a sequence. The trained NN could successfully replicate the performance of the GA on the benchmark problem. The developed NN scheduler was then tested against the GA, Attribute-Oriented Induction data mining methodology and common dispatching rules on a test set of randomly generated problems. The better performance of the NN scheduler on the test problem set compared to other methods proves the feasibility of NN-based scheduling. The scalability of the NN scheduler on larger problem sizes was also found to be satisfactory in replicating the performance of the GA. [ABSTRACT FROM AUTHOR]

To model complex systems for agent behaviors, genetic algorithms have been used to evolve neural networks which are based on cellular automata. These neural networks are popular tools in the artificial life community. This hybrid architecture aims at achieving synergy between the cellular automata and the powerful generalization capabilities of the neural networks. Evolutionary algorithms provide useful ways to learn about the structure of these neural networks, but the use of direct evolution in more difficult and complicated problems often fails to achieve satisfactory solutions. A more promising solution is to employ incremental evolution that reuses the solutions of easy tasks and applies these solutions to more difficult ones. Moreover, because the human brain can be divided into many behaviors with specific functionalities and because human beings can integrate these behaviors for high-level tasks, a biologically-inspired behavior selection mechanism is useful when combining these incrementally evolving basic behaviors. In this paper, an architecture based on cellular automata, neural networks, evolutionary algorithms, incremental evolution and a behavior selection mechanism is proposed to generate high-level behaviors for mobile robots. Experimental results with several simulations show the possibilities of the proposed architecture. [ABSTRACT FROM AUTHOR]

The nonconformance diagnosis problem has been a major issue facing industry and academia over the years. Research has been carried out on technologies for different aspects of nonconformance diagnosis such as nonconformance monitoring, prediction, prevention, classification, tracking, and recovery. Despite these advances, nonconformance tracking and recovery still receive many concerns due to the fact that they are knowledge intensive and experience-based tasks, which in complex manufacturing environments can sometimes be beyond the capabilities of skilled operators and engineers. In addition, the existing systems for nonconformance tracking and recovery are usually special purpose systems. They lack the capabilities to migrate to new working domains. This paper proposes a generic intelligent nonconformance tracking and recovery (GINTR) system. In conjunction with computational intelligent techniques such as Artificial Neural Networks (ANN) and Genetic Algorithm (GA), the system identifies the root causes of a nonconformance and provides timely corrective actions. The drive towards designing such a system is motivated by the need to implement a generic base of system capabilities that is reliable, economical, scalable, and provides a stable foundation for migrating the system to different domains. [ABSTRACT FROM AUTHOR]

In this paper, the optimisation of the EDM process parameters from the rough cutting stage to the finish cutting stage has been reported. A trained neural network was used to establish the relationship between the process parameters and machining performance. Genetic algorithms with properly defined objective functions were then adapted to the neural network to determine the optimal process parameters. Examples with specifications intentionally assigned the same values as those recorded in the database or selected arbitrarily have been fed into the developed GA-based neural network in order to verify the optimisation ability throughout the machining process. Accordingly, the optimised results indicate that the GA-based neural network can be successfully used to generate optimal process parameters from the rough cutting stage to the finish cutting stage. [ABSTRACT FROM AUTHOR]

The shortened time-to-market of new products requires experts from multiple disciplines to cooperate in product development. In this collaborative product development environment, an Internet-based conceptual design support system for Injection moulding is developed. It can help designers to put forward feasible design solutions quickly and provide strong support for collaborative design. The architecture, implementation and evaluation of this system are discussed in this paper. The proposed system makes fully use of component-based web technology and has a flexible distributed architecture. A hybrid neural network and genetic algorithm approach is also adopted for more accurate and efficient retrieval. Experiment results indicate it is superior to conventional systems in accuracy and time. [ABSTRACT FROM AUTHOR]

In this paper we present a new technique to simulate polymer blends that overcomes the shortcomings in polymer system modeling. This method has an inherent advantage in that the vast existing information on polymer systems forms a critical part in the design process. The stages in the design begin with selecting potential candidates for blending using Neural Networks. Generally the parent polymers of the blend need to have certain properties and if the blend is miscible then it will reflect the properties of the parents. Once this step is finished the entire problem is encoded into a genetic algorithm using various models as fitness functions. We select the lattice fluid model of Sanchez and Lacombe (J. Polym. Sci. Polym. Lett. Ed., vol. 15, p. 71, 1977), which allows for a compressible lattice. After reaching a steady-state with the genetic algorithm we transform the now stochastic problem that satisfies detailed balance and the condition of ergodicity to a Markov Chain of states. This is done by first creating a transition matrix, and then using it on the incidence vector obtained from the final populations of the genetic algorithm. The resulting vector is converted back into a population of individuals that can be searched to find the individuals with the best fitness values. A high degree of convergence not seen using the genetic algorithm alone is obtained. We check this method with known systems that are miscible and then use it to predict miscibility on several unknown systems. [ABSTRACT FROM AUTHOR]

Recognition of preconflict situations has a powerful potential for early warning of violent political conflicts. This paper focuses on the design and application of artificial neural networks as classifiers of preconflict situations. Achieving a desired level of performance of the neural network relies on the appropriate construction of recognition space (selection of indicators) and the choice of network architecture. A fast and effective method for the design of reliable neural recognition systems is described. It is based on genetic algorithm techniques and optimizes both the configuration of input space and the network parameters. The implementation of the methodology provides for increased performance of the classifier in terms of accuracy, generalization capacity, computational and data requirements. [ABSTRACT FROM AUTHOR]

In this study, we propose a new genetic algorithm that uses a statistical-based chromosome replacement strategy determined by the empirical distribution of the objective function values. The proposed genetic algorithm is further used in the training process of a multiplicative neuron model artificial neural network. The objective function value for the genetic algorithm is the root mean square error of the multiplicative neuron model artificial neural network prediction. This combination of methods is proposed for a particular type of problems, that is, time-series prediction. We use different subsets of three stock exchange time series to test the performance of the proposed method and compare it against similar approaches, and the results prove that the proposed genetic algorithm for the multiplicative neuron model of the artificial neural network works better than many other artificial intelligence optimization methods. The ranks of the proposed method are 1.78 for the Nikkei data sets, 1.55 for the S&P500 data sets and 1.22 for the DOW JONES data sets for data corresponding to different years, according to the root mean square error, respectively. Moreover, the overall mean rank is 1.50 for the proposed method. Also, the proposed method obtains the best performance overall as well as the best performance for all the individual tests. The results certify that our method is robust and efficient for the task investigated. [ABSTRACT FROM AUTHOR]

Incremental forming (IF) is one of the novel manufacturing processes that has gained much attention from researchers and practitioners. As a result, various analytical and numerical models of IF have been developed. The remarkable thing is that artificial intelligence (AI)-based computational methods have been used in solving IF-related problems. This study reviews the extant literature relevant to IF. It is found that AI techniques such as artificial neural networks, support vector regression, decision trees, fuzzy logic, genetic algorithms, particle swarm optimization have been used in solving IF-relevant problems. In addition, hybrid methods that combine some of the above-mentioned techniques have also been used. Moreover, it is shown that the performance parameters of IF such as springback and geometrical accuracy, formability, forming forces, surface roughness, forming time, and average deformed sheet thickness have been predicted and a few toolpath strategies have been developed using AI-based techniques. Thus, this study would serve researchers and practitioners who want to solve IF-related problems and advance the applicability of IF. [ABSTRACT FROM AUTHOR]

Welding is a basic manufacturing process for making components or assemblies. Recent welding economics research has focused on developing the reliable machinery database to ensure optimum production. It is an important issue, especially for the expensive equipment and the high quality preference in welding. An integrated approach is proposed to address the welding economic design problem. The integrated approach applies general regression neural network to approximate the relationship between welding parameters (welding current, electrode force, welding time, and sheet thickness) and the failure load. Analytical formula can be generated from the trained general regression neural network, and the mathematical model for the economic welding design can be constructed. An optimization method based on genetic algorithms is then applied to resolve the mathematical model and to select the optimum welding parameters. These parameters are used to obtain the preferred welding quality at the least possible cost. [ABSTRACT FROM AUTHOR]

Metaheuristics are the most exciting development in approximate optimization techniques of the last two decades. They have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas. This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics. Metaheuristics include but are not limited to constraint logic programming; greedy random adaptive search procedures; natural evolutionary computation; neural networks; non-monotonic search strategies; space-search methods; simulated annealing; tabu search; threshold algorithms and their hybrids. References are presented in alphabetical order under a number of subheadings. [ABSTRACT FROM AUTHOR]

In the current study, various evolutionary artificial intelligence and machine learning models namely, optimized artificial neural network (ANN), genetic algorithm optimized with ANN (GA-ANN) and particle swarm optimization optimized with ANN (PSO-ANN), differential evolution algorithm (DEA), adaptive neuro-fuzzy inference system (ANFIS), general regression neural network (GRNN), and feedforward neural network (FFNN) were optimized and applied to predict the ultimate bearing capacity (Fult) of shallow footing on two-layered soil condition. Due to a lot of input variables such as (upper layer thickness/foundation width (h/B) ratio, footing width (B), top and bottom soil layer properties) finding a reliable solution for such a complex engineering problem is difficult. Most of the available solutions are based on very limited experimental works. To assess the capability of proposed methods a new ranking system called CER (color intensity rating) based on their result of above indices was developed. As a result, although all provided methods, after being optimized, could successfully predict the bearing capacity of shallow footing in the two-layer subsoil and PSO-ANN could perform better compared to other techniques. Based on RMSE, R2 and VAF, values of (0.01, 0.99, and 99.90) and (0.01, 0.99, and 99.90) were found, respectively, for the training and testing datasets of PSO-ANN model. In this regard, the accuracy of other hybrid algorithm of GA-ANN model with RMSE, R2 and VAF of (0.05, 0.99, and 97.80) and (0.06, 0.99, and 97.57), respectively, for the training and testing datasets was slightly lower than the PSO-ANN model. This shows the superiority of the PSO-ANN model in the prediction of a highly complex real-world engineering problem. [ABSTRACT FROM AUTHOR]

Credit analysts generally assess the risk of credit applications based on their previous experience. They frequently employ quantitative methods to this end. Among the methods used, Artificial Neural Networks have been particularly successful and have been incorporated into several computational tools. However, the design of efficient Artificial Neural Networks is largely affected by the definition of adequate values for their free parameters. This article discusses a new approach to the design of a particular Artificial Neural Networks model, RBF networks, through Genetic Algorithms. It presents an overall view of the problems involved and the different approaches employed to optimize Artificial Neural Networks genetically. For such, several methods proposed in the literature for optimizing RBF networks using Genetic Algorithms are discussed. Finally, the model proposed by the authors is described and experimental results using this model for a credit risk assessment problem are presented. [ABSTRACT FROM AUTHOR]

This work examines the possibility of using genetic algorithms and some neural networks to optimise mechanisms. A detailed example shows that using this stochastic method along with neural networks is very efficient. We can thus speak of a metamodel for optimisation in the context of integrated design . [ABSTRACT FROM AUTHOR]

The objective and accurate evaluation of land value is one of the biggest interest issues that farmers are concerned about in the transfer of rural land, and it is one of the influencing factors that influence the country's rural rejuvenation strategy recommendation. Considering the nonlinear characteristics of various policy and social issues, such as the nature of land and market chaos in the process of rural land circulation, neural networks are introduced to study rural land evaluation based on the development of wireless networks, and the mechanism of influence of promotion of rural regeneration is explored. After analyzing the structure and flow of the neural network, the related equations of the BP neural network are updated, and the introduction of a genetic algorithm to establish a hybrid model is proposed to promote the effective evaluation of the complexity problem by the BP neural network algorithm and improve the prediction accuracy. After simulation experiments show that this study can provide a fair evaluation of the value of rural land transfer, and it has positive significance for promoting the development strategy of rural rejuvenation. [ABSTRACT FROM AUTHOR]