A new approach for pattern recognition with Neuro-Genetic system using Microbial Genetic Algorithm

This paper proposes a new method for the design, through simulated evolution, of biologically inspired receptive fields in feedforward neural networks (NNs). The method is intended to enhance pattern recognition performance by creating new neural architectures specifically tuned for a particular pattern recognition problem. It proposes a combined neural architecture composed of two networks in cascade: a feature extraction network (FEN) followed by a neural classifier. The FEN is composed of several layers with receptive fields constructed by additive superposition of excitatory and inhibitory fields. A genetic algorithm (GA) is used to select receptive field parameters to improve classification performance. The parameters are receptive field size, orientation, and bias as well as the number of different receptive fields in each layer. Based on a random initial population where each individual represents a different neural architecture, the GA creates new enhanced individuals. The method is applied to handwritten digit classification and face recognition. In both problems, results show strong dependency between NN classification performance and receptive field architecture. GA selected parameters of the receptive fields produced improvements in the classification performance on the test set up to 90.8% for the problem of handwritten digit classification and up to 84.2% for the face recognition problem. On the same test sets, results were compared advantageously to standard feedforward multilayer perceptron (MLP) NNs where receptive fields are not explicitly defined. The MLP reached a maximum classification performance of 84.9% and 77.5% in both problems, respectively.

From the Publisher: Solving pattern recognition problems involves an enormous amount ofcomputational effort. By applying genetic algorithms - a computational method based on the way chromosomes in DNA recombine - these problems are more efficiently and more accurately solved. Genetic Algorithms for Pattern Recognition covers a broad range of applications in science and technology, describing the integration of genetic algorithms in pattern recognition and machine learning problems to build intelligent recognition systems. The articles, written by leading experts from around the world, accomplish several objectives: they provide insight into the theory of genetic algorithms; they develop pattern recognition theory in light of genetic algorithms; and they illustrate applications in artificial neural networks and fuzzy logic. The cross-sectional view of current research presented in Genetic Algorithms for Pattern Recognition makes it a unique text, ideal for graduate students and researchers.

In order to overcome the drawbacks of Simple Genetic Algorithm such as cannot get the most optimal result, low convergence speed et al. Cellular Simple Genetic Algorithm-a new genetic algorithm based on Cellular Automata-is presented in this paper. Compared with the Simple Genetic Algorithm, the experiment results show the Cellular Simple Genetic Algorithm has remarkable advantages in following aspects: reducing the search-time and improving the precise of target function.

When using genetic and evolutionary algorithms for network design, choosing a good representation scheme for the construction of the genotype is important for algorithm performance. One of the most common representation schemes for networks is the characteristic vector representation. However, with encoding trees, and using crossover and mutation, invalid individuals occur that are either under- or over-specified. When constructing the offspring or repairing the invalid individuals that do not represent a tree, it is impossible to distinguish between the importance of the links that should be used. These problems can be overcome by transferring the concept of random keys from scheduling and ordering problems to the encoding of trees. This paper investigates the performance of a simple genetic algorithm (SGA) using network random keys (NetKeys) for the one-max tree and a real-world problem. The comparison between the network random keys and the characteristic vector encoding shows that despite the effects of stealth mutation, which favors the characteristic vector representation, selectorecombinative SGAs with NetKeys have some advantages for small and easy optimization problems. With more complex problems, SGAs with network random keys significantly outperform SGAs using characteristic vectors. This paper shows that random keys can be used for the encoding of trees, and that genetic algorithms using network random keys are able to solve complex tree problems much faster than when using the characteristic vector. Users should therefore be encouraged to use network random keys for the representation of trees.

This paper presents a crowding niche cellular genetic algorithm (referred to NCGA) aiming at solving the problems of local convergence and non-uniform population distribution in traditional genetic algorithm. The selecting operation in traditional genetic algorithm is improved by bringing in the concept of neighbors of cellular genetic algorithm, and the population distribution is greatly enhanced by introducing crowding niche mechanism, which betters the ability of global searching and helps to avoid the population local convergence. Meanwhile, the paper describes the crowding niche cellular genetic algorithm in details and compares it with simple genetic algorithm （SGA） and simple niche genetic algorithm （NGA）; the comparison results reveal that, NCGA outperforms the other two algorithms in terms of convergence rate and population diversity.

The Graph Partitioning Problem (GPP) is one of the fundamental multimodal combinatorial problems that has many applications in computer science. Many algorithms have been devised to obtain a reasonable approximate solution for the GP problem. This paper applies different Genetic Algorithms in solving GP problem. In addition to using the Simple Genetic Algorithm (SGA), it introduces a new genetic algorithm named the Adaptive Population Genetic Algorithm (APGA) that overcomes the premature convergence of SGA. The paper also presents a new approach using niching methods for solving GPP as a multimodal optimization problem. The paper also presents a comparison between the four genetic algorithms; Simple Genetic Algorithm (SGA), Adaptive Population Genetic Algorithm (APGA) and the two niching methods; Sharing and Deterministic Crowding. when applied to the graph partitioning problem. Results proved the superiority of APGA over SGA and the ability of niching methods in obtaining a set of multiple good solutions.KeywordsGenetic AlgorithmPremature ConvergenceTournament SelectionSimple Genetic AlgorithmSimilar IndividualThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper addresses processing of spectrophotometric array signals based on genetic algorithms (GA) least square support vector machines (LS-SVM) regression to provide a powerful model for machine learning and data mining. The key to complete LS-SVM regression is to choose its optimal parameters. Due to their outstanding ability in solving global optimization problems in complex multidimensional search space, GA are used in this study to obtain the optimal parameter combination of the LS-SVM model. Experimental results showed the GA-LS-SVM method to be successful for simultaneous multicomponent determination even where severe overlap of spectra was present. KeywordsLeast squares support vector machines; Genetic algorithms; Spectrophotometric array signals; Overlapping spectra; Artificial intelligence Nowadays, with the application of photometric diode array detector and computers, rapid scanning commercial spectrophotometers are capable of quickly generating huge data consisting of hundreds and even thousands of absorbance values per spectrum. The array data named fullspectrum contain sufficient information to be able to determine the contents of various compounds. The main drawback of ultraviolet-visible (UV-VIS) is its poor selectivity because in many cases UV-VIS spectra display strong overlaps, especially some less specific and selective chromagenic reagents often give rise to strongly overlapped spectra in many cases. The combination of artificial intelligence methods with the computer-controlled spectrophotometers was proven to be effective in overcoming this difficulty [1-3]. Artificial neural network (ANN) is a form of artificial intelligence that mathematically simulates biological nervous system [4, 5]. However, ANN often has slow convergence, is prone to the existence of many local minima during training, and has a tendency of overfitting. Recently, a promising technology called support vector machines (SVM) has been used for classification and regression problems. SVM pioneered by Vapnik is a kind of machine learning method based on modern statistical learning theory and has notable properties including absence of local minima and high generalization ability [6, 7]. Suykens and his coworkers [8] introduced a modified version of SVM called least square SVM (LS-SVM) , which requires solving a set of linear equations instead of a quadratic programming problem and is much easier and computationally simpler than SVM. SVM and LS-SVM represent relatively recent artificial intelligence method and have found some applications in image analysis, classification and disease diagnosis etc. [9, 10]. It is worth mentioning that the success of LS-SVM model is highly dependent on the optimum choice of two parameters, the relative weight of regression error γ and the kernel width σ of radial basis function (RBF). Genetic algorithms (GA) [11, 12] introduced by John Holland are probabilistic optimization techniques based on natural evolution and genetics and Darwin’s theory of survival of the best. With their efficient and robust global search ability, GA are used to search two optimal parameters for the LS-SVM model simultaneously and automatically. The LS-SVM model then performs the regression task using these optimal parameters.

The transportation system has become a fascinating and active research topic due to its multiple problems; most prior research has focused on traffic forecasting, the advanced driver assistant system (ADAS), and self-driving vehicles. Traffic sign recognition (TSR) is an essential sub-system in ADAS that helps a driver better understand the surrounding environment (obstacles, frost, pedestrians). Automatic recognition of traffic signs is a real-world computer vision challenge and pattern recognition problem. Recently, deep architecture neural networks have shown robust solutions in many areas (health care, agriculture, transportation) due to their ability to handle large amounts of data and excel in complex systems. Therefore, a convolutional neural network (CNN) has been adopted, one of the best deep learning approaches in pattern recognition and image classification for TSR. The proposed architecture has been trained and tested on the German traffic sign recognition benchmark dataset (GTSRB). The results reported accuracy of 99.43% that outperformed human accuracy.KeywordsTraffic sign recognition (TSR)Advanced driver assistant system (ADAS)Deep learningConvolutional neural network (CNN)

The genetic algorithm(GA) is efficient method for topological optimization problem of truss structures because it is possible to obtain global optimum on non-convex design space. The bottleneck of this method is that a large number of function evaluations and structural analyses are necessarily. Performance of the GA depends on first arrangement of string positions and the crossover operator strongly when the simple GA apply to the topological optimization problems. If inadequate correspondence between design variables and the string positions is used, good solution is hardly obtained in spite of numerous calculations. The objective of this study is development a new genetic algorithm specialized to the topological optimization problem of the truss structure improving the calculation efficiency and reliability to obtain optimum solution. To overcome disadvantage of the simple GA, biased crossover operation with string position rearrangement is proposed. Highly fitted gene is crated and inherited by using this procedure. Minimum weight design of the truss structure consists of nine nodes are carried out by using simple G A and proposed GA, and calculation performance is compared. From the result, it is confirmed that the proposed GA is more effective and reliable in comparison with the simple GA.

Most of the well‐known clustering methods based on distance measures, distance metrics and similarity functions have the main problem of getting stuck in the local optima and their performance strongly depends on the initial values of the cluster centers. This paper presents a new approach to enhance the clustering problems with the bio‐inspired Cuttlefish Algorithm (CFA) by searching the best cluster centers that can minimize the clustering metrics. Various UCI Machine Learning Repository datasets are used to test and evaluate the performance of the proposed method. For the sake of comparison, we have also analysed several algorithms such as K‐means, Genetic Algorithm and the Particle Swarm Optimization (PSO) Algorithm. The simulations and obtained results demonstrate that the performance of the proposed CFA‐Clustering method is superior to the other counterpart algorithms in most cases. Therefore, the CFA can be considered as an alternative stochastic method to solve clustering problems.

Accurate fault diagnosis for large machines is very important for its economic meaning. In essence, fault diagnosis is a pattern classification and recognition problem of judging whether the operation status of the system is normal or not. Support vector machines (SVMs), well motivated theoretically, have been introduced as effective methods for solving classification problems. However, the generalization performance of SVMs is sometimes far from the expected level due to their limitations in practical applications. Ensemble learning of SVMs provides a promising way for such cases. In this paper, a new ensemble learning method based on genetic algorithm is presented. Genetic algorithm is introduced into ensemble learning so as to search for accurate and diverse classifiers to construct a good ensemble. The presented method works on a higher level and is more direct than other search based ensemble learning methods. Different strategies of constructing diverse base classifiers are also studied. Experimental results on a steam turbine fault diagnosis problem show that the presented method can achieve much better generalization performance than other methods including single SVM, Bagging and Adaboost.M1 if the strategies used are appropriate for the practical problem.

This paper presents the fusion of artificial intelligence (AI) learning algorithms that are genetic algorithms (GA) and conjugate gradient (CG) methods. Both methods are used to find the optimum weights for the hidden and output layers of feedforward artificial neural network (ANN) model. Each algorithm is presented in separate module and we proposed three different types of Dynamic Connection Strategies (DyConS) for combining both algorithms to improve the recognition performance of spoken Malay speech recognition. Two different GA techniques are used in this research: a mutated GA technique is proposed and compared with the standard GA technique. One hundred experiments with 5000 words are conducted using the proposed DyConS. Owing to previous facts, GA combined with ANN proved to attain certain advantages with sufficient recognition performance. Thus, from the results, it was observed that the performance of mutated GA algorithm when combined with CG is better than standard GA and CG models. Integrating the GA with feed-forward network improved mean square error (MSE) performance and with good connection strategy by this two stage training scheme, the recognition rate is increased up to 99%.

Military antenna design using simple and competent genetic algorithms

We address the problem of routing mobile agent with fusion cost in WSN under two scenarios: all source nodes, and a part of source nodes to be visited, respectively. Two objective functions in terms of minimizing transmission and fusion energy expenditure in the two scenarios are formulated, and proved to be NP-complete. Furthermore, we propose a two-level encoding based genetic algorithm (GA) to compute a suboptimal solution for the first scenario, and for the second scenario, we propose two computation paradigms to obtain a suboptimum: three-level encoding based GA, and two-step GA which consists of a two-level encoding GA and a simple GA. Simulation results show that proposed GA outperforms local closest first method (LCF) with different unit fusion costs and data reduction ratios in the first scenario. In the second scenario, simulation results with different conditions show that proposed algorithms are feasible, and three-level encoding based GA outperforms two-step GA in solution quality but the latter has superior performance in convergence speed.

This work investigates the problem of finding near-optimal strategies for the bilateral negotiation with incomplete information between two competitive negotiation agents for resolving the differences in their objectives and preferences. While there are some former studies of finding negotiation strategies using evolutionary approaches, there are extremely few works on effectively locating the global optimal solution. In this study, we use a genetic algorithm (GA) to find near-optimal negotiation strategies for both negotiation agents conducting bilateral negotiation by coevolving both agents' strategies. Agents learn optimal strategies through trial-and-error, which is done by matching and negotiation of agents of one population with agents of the other population through random pairing in a one-to-one manner. However, a simple GA often cannot find the optimum results for both agents for the following reasons: (i) the premature convergence of a simple GA in itself, and (ii) both parties' failure to keep pace with each other's learning speed in the coevolution. To solve this problem, this paper proposes a GA which has a novel dynamic diversity controlling capability. The proposed method utilizes the accumulated frequency information of the occurrence of individuals in each band of the population for generations. The information is used in a diversification and refinement procedure of the proposed diversity controlling GA. Empirical results show that the proposed dynamic diversity controlling GA generally outperforms the traditional GA for finding near-optimal negotiation strategies.