Genetic Algorithms and Clustering: an Application to Fisher’s Iris Data

The paper proposes a fast online learning method for neural network structures by using genetic algorithm (GA) and dynamic back propagation algorithm (BP) jointly. GA is used in the coarse tuning process which adjusts interconnection weights of the neural network. The dynamic back propagation algorithm is subsequently applied to achieve fine adjusting of the network weights. The fitness function, based on the squared error between the teaching signal and the network output value, is redefined at every time step and the proposed GA based algorithm solves a nonstationary function optimization task. At every time step the solution with the best fitness function is used for current representation of the neural network weights and biases. It is shown through the simulations and real time temperature control of drying oven that this learning algorithm has faster convergence ability and better performance on reducing mapping error in the online learning neural network structures. This leads to an improvement of the transient response of neuro adaptive systems. The proposed method has the potential to be applied to many practical areas such as system modeling and control, signal processing and pattern recognition.

Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation

Composite beams, plates and shells are widely used in the aerospace industry because of their advantages over the commonly used isotropic structures especially when it comes to weight savings. Buckling analyses of composite structural components must be performed in order to ensure, for instance, that a composite panel designed to be part of a control surface does not buckle thereby compromising its aerodynamic shape. Optimization of composite structures has been performed in this paper using Genetic algorithm. Genetic algorithm (GA) approaches are successfully implemented for the TSP. The buckling load of composite plate, which is obtained by the Artificial Neural Networks, was used as the fitness function in the GA to find its optimized value by arranging the ply stacking sequence.

In this paper, a neuro-genetic approach is proposed for the inverse kinematics problem solution of robotic manipulators. The proposed solution method is based on using neural networks and genetic algorithms in a hybrid system. Neural networks have been used by many researchers in the inverse kinematics solution. Since the neural networks work with an acceptable error, the error at the end of learning has to be minimized for sensitive applications. This study is based on using genetic algorithms to minimize this error. A case study is presented for a 6 degree of freedom robot. In the neural network part, three Elman networks are separately trained and then used in parallel since one Elman network may give better result than the other two ones. These three results are placed in the initial population of the genetic algorithm. The end effector position error is defined as the fitness function and genetic algorithm is implemented. Thus, the error is reduced in micrometer levels. Key words: Elman neural networks, error minimization, six-degree-of-freedom robot, genetic algorithms, robotics.

The research presented in this paper builds on previous work that lead to the definition of a family of semantic relatedness algorithms that compute a proximity given as input a pair of concept labels. The algorithms depends on a semantic graph, provided as RDF data, and on a particular set of weights assigned to the properties of RDF statements (types of arcs in the RDF graph). The current research objective is to automatically tune the weights for a given graph in order to increase the proximity quality. The quality of a semantic relatedness method is usually measured against a benchmark data set. The results produced by the method are compared with those on the benchmark using the Spearman's rank coefficient. This methodology works the other way round and uses this coefficient to tune the proximity weights. The tuning process is controlled by a genetic algorithm using the Spearman's rank coefficient as the fitness function. The genetic algorithm has its own set of parameters which also need to be tuned. Bootstrapping is based on a statistical method for generating samples that is used in this methodology to enable a large number of repetitions of the genetic algorithm, exploring the results of alternative parameter settings. This approach raises several technical challenges due to its computational complexity. This paper provides details on the techniques used to speedup this process. The proposed approach was validated with the WordNet 2.0 and the WordSim-353 data set. Several ranges of parameters values were tested and the obtained results are better than the state of the art methods for computing semantic relatedness using the WordNet 2.0, with the advantage of not requiring any domain knowledge of the ontological graph.

Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network model of environemntal information in the workspace for a robot and used this model to establish the relationship between a collision avoidance path and the output of the model. Then the two-dimensional coding for the path via-points was converted to one-dimensional one and the fitness of both the collision avoidance path and the shortest distance are integrated into a fitness function. The simulation results showed that the proposed method is correct and effective.

SUMMARY The seismic waveform inversion problem is usually cast into the framework of Bayesian statistics in which prior information on the model parameters is combined with the data and physics of the forward problem to estimate the a posteriori probability density (PPD) in model space. The PPD is a function of an objective or fitness function computed from the observed and synthetic data. In general, the PPD or the fitness function is multimodal and its shape is unknown. Global optimization methods such as simulated annealing (SA) and genetic algorithms (GA) do not require that the shape of the fitness function be known. In this paper, we investigate GA to rapidly sample the most significant portion or portions of the PPD, when very little prior information is available. First, we use a simple three operator (selection, crossover and mutation) GA acting on a randomly chosen finite population of haploid binary coded models. We use plane wave transformed synthetic seismic data and a normalized cross-correlation function [E(m)] in the frequency domain as a fitness function. A moderate value of crossover probability, a low value of mutation probability, a high value of update probability and a proper population size are required to reach very close to the global maximum of the fitness function. Next, with an attempt to accelerate convergence we show that the concepts from simulated annealing can be used in stretching of the fitness function, i.e., we use exp [E(m)/T] rather than E(m) as the fitness function, where T is a control parameter analogous to temperature in simulated annealing. By a schemata analysis, we show that at low temperatures, schemata with above average fitness values are reproduced in large numbers causing a much more rapid convergence of the algorithm. A high value of temperature T assigns nearly equal selection probability to most of the schemata and thus retains diversity among the members of the population. Thus a GA with a step function type cooling schedule (very high temperature in the beginning followed by rapid cooling to a very low temperature) improves the performance dramatically: high values of the fitness function are obtained rapidly using only half as many models as would be required by a conventional GA. Similar performance could also be achieved by first using a high mutation probability and then decreasing the mutation probability to a very low value, while retaining the same low temperature throughout. We also address the problem of ‘genetic drift’ which causes the finite GAs to converge to one peak or the other when the algorithm is applied to a highly multimodal fitness function with several peaks of nearly the same height. A parallel genetic algorithm based on the concept of ‘punctuated equilibria’ is implemented to circumvent the problem. We run several GAs each with a finite subpopulation in parallel and collect many good models from each one of these runs. These are then used to grasp the most significant portion(s) of the PPD in model space. We then compute the weighted mean model and use the derived good models to estimate uncertainty in the derived model parameters.

On the study of kansei engineering and product image evaluation, a product image styling design method based on neural network and genetic algorithm is presented. Firstly, according to the findings of kansei image, image evaluating pattern with neural network is constructed; and then, according to consumers' kansei image demands and cooperated with evaluating pattern of neural network as genetic filial generation's fitness function, the model of product image styling design with genetic algorithm is developed. It can automatically reason out the conceptual image styling product which will well satisfy consumers' psychical demands. Finally, the colour design of MP3 is taken to examine and certify the verification of the method. However, the proposed method is equally applicable to the styling design of other products.

Among the emerging technologies nowadays, the genetic algorithm, a powerful optimization technique, is becoming the subject of new craze among neural network researchers. Genetic algorithms (GAs) for training and designing artificial neural networks (ANNs) have proved to be a useful integration. This paper reports an improvement over earlier work on the genetic evolution of neural network weights using the two-parents multipoint restricted crossover (Double-MRX) operator proposed by Srivastava, Shukla and Srivastava (Microelectronics Journal, vol. 29, no. 11, p.921-31, 1998). In this research, a methodology to improve network convergence is presented by introducing a new concept contrary to natural law, i.e. crossover with randomly selected multiple crossover sites restricted to lie within individual weight boundaries, hence termed as Triple-MRN. In GAs, the search strategy relies more on exchange of information between individual building blocks by exploiting crossover operator. The use of Triple-MRX promotes cooperation among individuals, that better exploits the new genotypic information contained in genome variation. This ensures a much more effective search, both in terms of quality of the solution and speed of convergence as shown by the simulation experiments. Fitness function used in the authors' study is 1/MSE (mean square error). The effectiveness of the proposed technique is tested by evaluating the capability of neural network to learn a real-world gas identification problem.

The Sixth International Symposium on Neural Networks (ISNN 2009)

Genetic Algorithm (GA) classifier can automatically search a proper clustering number according to fitness evaluation instead of assignment by users. In this study, a GA classifier with various fitness functions is adopted to search the cluster centers and a suitable cluster number for digital images to overcome the disadvantages of the conventional unsupervised classifier. By employing a proper clustering index as fitness, a GA with length-variable chromosome can determine the most suitable number of clusters and the most proper cluster centers.

Rule extraction from data is one of the key technologies for solving the bottlenecks in artificial intelligence. Artificial neural networks are well suited for representing any knowledge in given data. Extraction of logical/fuzzy rules from the trained artificial neural network is of great importance to researchers in the fields of artificial intelligence and soft computing. Fuzzy rule sets are capable of approximating any nonlinear mapping relationships. Extraction of rules from data has been discussed in terms of fuzzy modeling, fuzzy clustering, and classification with fuzzy rule sets. This special issue entitled"Rule Extraction from Data" is aimed at providing the readers with good insights into the advanced studies in the field of rule extraction from data using neural networks/fuzzy rule sets. I invited seven research papers best suited for the theme of this special issue. All the papers were reviewed rigorously by two reviewers each. The first paper proposes an interesting rule extraction method from data using neural networks. Ishikawa presents a combination of learning with an immediate critic and a structural learning with forgetting. This method is capable of generating skeletal networks for logical rule extraction from data with correct and wrong answers. The proposed method is applied to rule extraction from lense data. The second paper presents a new methodology for logical rule extraction based on transformation of MLP (multilayered perceptron) to a logical network. Duck et al. applied their C-MLP2LN to the Iris benchmark classification problem as well as real-world medical data with very good results. In the third paper, Geczy and Usui propose fuzzy rule extraction from trained artificial neural networks. The proposed algorithm is implied from their theoretical study, not from heuristics. Their study enables to initially consider derivation of crisp rules from trained artificial neural network, and in case of conflict, application of fuzzy rules. The proposed algorithm is experimentally demonstrated with the Iris benchmark classification problem. The fourth paper presents a new framework for fuzzy modeling using genetic algorithm. The authors have broken new ground of fuzzy rule extraction from neural networks. For the fuzzy modeling, they have proposed a particular type of neural networks containing nodes representing membership functions. In this fourth paper, the authors discuss input variable selection for the fuzzy modeling under multiple criteria with different importance. A target system with a strong nonlinearity is used for demonstrating the proposed method. Kasabov, et al. present, in the fifth paper, a method for extraction of fuzzy rules that have different level of abstraction depending on several modifiable thresholds. Explanation quality becomes better with higher threshold values. They apply the proposed method to the Iris benchmark classification problem and to a real world problem. J. Yen and W. Gillespie address interpretability issue of Takagi-Sugeno-Kang model, one of the most popular fuzzy mdoels, in the fifth paper. They propose a new approach of fuzzy modeling that ensures not only a high approximation of the input-output relationship in the data, but also good insights about the local behavior of the model. The proposed method is applied to fuzzy modeling of sinc function and Mackey-Glass chaotic time series data. The last paper discusses fuzzy rule extraction from numerical data for high-dimensional classification problems. H.Ishibuchi, et al. have been pioneering methods for classification of data using fuzzy rules and genetic algorithm. In this last paper, they introduced a new criterion, simplicity of each rule, together with the conventional ones, compactness of rule base and classification ability, for high-dimensional problem. The Iris data is used for demonstrating their new classification method. They applied it also to wine data and credit data. I hope that the readers will be encouraged to explore the frontier to establish a new paradigm in the field of knowledge representation and rule extraction.

Location‐only and use‐availability data: analysis methods converge

In the present paper, optimization of functions with uncertainty by means of Genetic Algorithms (GA) is discussed. For such problems, there have been proposed methods of sampling fitness function several times and taking average of them for evaluation of each individual. However, important applications having uncertain fitness functions are online adaptation of real systems and complicated computer simulation using random variables. In such applications, possible number of fitness evaluation is quite limited. Hence, methods achieving optimization with less number of fitness evaluation is needed. In the present paper, the authors propose a GA for optimization of continuous fitness functions with observation noise utilizing history of search so as to reduce number of fitness evaluation. In the proposed method, value of fitness function at a novel search point is estimated not only by the sampled fitness value at that point but also by utilizing the fitness values of individuals stored in the history of search. Computer experiments using quadric fitness functions show that the proposed method outperforms the conventional GA of sampling fitness values several times at each search point in noisy environment.

In order to enhance the acoustical performance of gun mufflers, a multi-chamber muffler internally inserting with expansion cones and perforated tubes ducts is presented. Because of the complicated geometry of gun muffler, traditional analytic method used in predicting a muffler’s acoustical performance is difficult; therefore, a finite element method using the COMSOL software is adopted to predict the gun muffler’s sound transmission loss. Moreover, because an objective function (or fitness function) is needed during the optimization process, a simplified mathematical model using Artificial Neural Network (ANN) is used. Here, a polynomial objective function is established by inputting the muffler’s geometric design parameters and the related simulated Sound Transmission Loss (STL) (run on the COMSOL) via the Artificial Neural Network. Furthermore, Genetic Algorithm (GA), an optimizer, is linked to the simplified mathematical model to maximize the gun muffler’s acoustical performance. To pursue a maximal STL at the targeted tones, the geometric parameters of cone’s horizontal length (L1) and first cone’s span (L2) is optimally adjusted. Results demonstrates the STL of gun muffler at the specified frequencies of 1000, 2000, and 4000 Hz can be improved from 15.8 dB, 72.6 dB, 184.8 dB to 18.2 dB, 112.6 dB, and 416.3 dB, respectively. The paper aims to provide an optimal design method of gun muffler using Finite Element Method (FEM), Artificial Neural Network, and Genetic Algorithm. Consequently, the simulated results reveal that the optimally shaped gun muffler can efficiently depress the gun’s shooting noise.