Point pattern matching (PPM) arises in areas such as pattern recognition, digital video processing and computer vision. In this study, a novel Genetic Algorithm (GA) based method for matching affine-related point sets is described. Most common techniques for solving the PPM problem, consist in determining the correspondence between points localized spatially within two sets and then find the proper transformation parameters, using a set of equations. In this paper, we use this fact that the correspondence and transformation matrices are two unitary polar factors of Grammian matrices. We estimate one of these factors by the GA's population and then evaluate this estimation by computing an error function using another factor. This approach is an easily implemented one and because of using the GA in it, its computational complexity is lower than other known methods. Simulation results on synthetic and real point patterns with varying amount of noise, confirm that the algorithm is very effective.

This paper proposes the combination of adjustable architecture and parameter optimization software, employing a method based on artificial intelligence (AI), to realize an image rejection mixer (IRM) that can enhance its image rejection ratio within a short period of time. The main components of the IRM are 6 Gilbert-cell multipliers. The tail current of each multiplier is adjusted by the optimization software, and the gain and phase characteristics are optimized. This adjustment is conventionally extremely difficult because the 6 tail currents to be adjusted simultaneously are mutually interdependent. In order to execute this adjustment efficiently, we employed a Genetic Algorithm (GA) that is a robust search algorithm that can find optimal parameter settings in a short time. We have successfully developed an IRM chip that has a performance of 71 dB and is suitable for single-chip integration with WCDMA applications.

Recent work has shown that the usage of multiple antennas at the transmitter and receiver in a flat fading environment results in a linear increase in channel capacity. But increasing the number of antennas induces the higher hardware costs and computational burden. To overcome those problems, it is effective to select antennas appropriately among all available ones. In this paper, a new antenna selection method is proposed. The transmit antennas are selected so as to maximize the channel capacity using the genetic algorithm (GA) which is the one of the general random search algorithm. The results show that the proposed GA achieves almost the same performance as the optimal selection method with less computational amount.

An improved genetic algorithm for solving the graph planarization problem is presented. The improved genetic algorithm which is designed to embed a graph on a plane, performs crossover and mutation conditionally instead of probability. The improved genetic algorithm is verified by a large number of simulation runs and compared with other algorithms. The experimental results show that the improved genetic algorithm performs remarkably well and outperforms its competitors.

Genetic Algorithm (GA) and other Evolutionary Algorithms (EAs) have been successfully applied to solve constrained minimum spanning tree (MST) problems of the communication network design and also have been used extensively in a wide variety of communication network design problems. Choosing an appropriate representation of candidate solutions to the problem is the essential issue for applying GAs to solve real world network design problems, since the encoding and the interaction of the encoding with the crossover and mutation operators have strongly influence on the success of GAs. In this paper, we investigate a new encoding crossover and mutation operators on the performance of GAs to design of minimum spanning tree problem. Based on the performance analysis of these encoding methods in GAs, we improve predecessor-based encoding, in which initialization depends on an underlying random spanning-tree algorithm. The proposed crossover and mutation operators offer locality, heritability, and computational efficiency. We compare with the approach to others that encode candidate spanning trees via the Pr?fer number-based encoding, edge set-based encoding, and demonstrate better results on larger instances for the communication spanning tree design problems.

A discriminative modeling is applied to optimize the structure of a Partly-Hidden Markov Model (PHMM). PHMM was proposed in our previous work to deal with the complicated temporal changes of acoustic features. It can represent observation dependent behaviors in both observations and state transitions. In the formulation of the previous PHMM, we used a common structure for all models. However, it is expected that the optimal structure which gives the best performance differs from category to category. In this paper, we designed a new structure optimization method in which the dependence of the states and the observations of PHMM are optimally defined according to each model using the weighted likelihood-ratio maximization (WLRM) criterion. The WLRM criterion gives high discriminability between the correct category and the incorrect categories. Therefore it gives model structures with good discriminative performance. We define the model structure combination which satisfy the WLRM criterion for any possible structure combinations as the optimal structures. A genetic algorithm is also applied to the adequate approximation of a full search. With results of continuous lecture talk speech recognition, the effectiveness of the proposed structure optimization is shown: it reduced the word errors compared to HMM and PHMM with a common structure for all models.

A new wideband signal DOA estimation algorithm based on modified quantum genetic algorithm (MQGA) is proposed in the presence of the errors and the mutual coupling between array elements. In the algorithm, the narrowband signal subspace fitting method is generalized to wideband signal DOA finding according to the character of space spectrum of wideband signal, and so the rule function is constructed. Then, the solutions is encoded onto chromosomes as a string of binary sequence, the variable quantum rotation angle is defined according to the distribution of optimization solutions. Finally, we use the MQGA algorithm to solve the nonlinear global azimuths optimization problem, and get optimization azimuths by fitness values. The computer simulation results illustrated that the new algorithm have good estimation performance.

Genetic algorithms are a general problem-solving technique that has been widely used in computational biology. In this paper, we present a framework to map hierarchical parallel genetic algorithms for protein folding problems onto computational grids. By using this framework, the two level communication parts of hierarchical parallel genetic algorithms are separated. Thus both parts of the algorithm can evolve independently. This permits users to experiment with alternative communication models on different levels conveniently. The underlying programming techniques are based on generic programming, a programming technique suited for the generic representation of abstract concepts. This allows the framework to be built in a generic way at application level and thus provides good extensibility and flexibility. Experiments show that it can lead to significant runtime savings on PC clusters and computational grids.

This paper proposes a genetically inspired adaptive clustering algorithm for numerical and categorical data sets. To this end, unique encoding method and fitness functions are developed. The algorithm automatically discovers the actual number of clusters and efficiently performs clustering without unduly compromising cluster-purity. Moreover, it outperforms existing clustering algorithms.

In order to simultaneously combat both of the inter-carrier interferences (ICIs) and multiple access interferences (MAIs) to achieve reliable performance in multi-carrier code division multiple access (MC-CDMA) systems, this letter proposes a maximum likelihood based scheme for joint frequency offset estimation and multiuser symbol detection. To reduce the computational complexity called for by the joint decision statistic without extra mechanisms, the genetic algorithm (GA) is employed to solve the nonlinear optimization involved. Due to the robustness of the GA, the joint decision statistic can be efficiently solved, and, as shown by furnished simulation results, the proposed approach can offer satisfactory performance in various scenarios.

We propose a learning method combining query learning and a "genetic translator" we previously developed. Query learning is a useful technique for high-accuracy, high-speed learning and reduction of training sample size. However, it has not been applied to practical optical character readers (OCRs) because human beings cannot recognize queries as character images in the feature space used in practical OCR devices. We previously proposed a character image reconstruction method using a genetic algorithm. This method is applied as a "translator" from feature space for query learning of character recognition. The results of an experiment with hand-written numeral recognition show the possibility of training sample size reduction.

This paper presents a novel method of structure selection and identification for Hammerstein type nonlinear systems. An unknown nonlinear static part to be estimated is approximately represented by an automatic choosing function (ACF) model. The connection coefficients of the ACF and the system parameters of the linear dynamic part are estimated by the linear least-squares method. The adjusting parameters for the ACF model structure, i.e. the number and widths of the subdomains and the shape of the ACF are properly selected by using a genetic algorithm, in which the Akaike information criterion is utilized as the fitness value function. The effectiveness of the proposed method is confirmed through numerical experiments.

Anycast refers to the transmission of data from a source node to (any) one member in the group of designed recipients in a network. When the physical network and the set of anycast requests are given, the Anycast Routing and Wavelength Assignment (ARWA) problem is to find a set of light-paths, one for each source, for anycasting messages to any one of the member in the anycast destination group such that not any path using the same wavelength passes through the same link. The goal of the ARWA problem is to minimize the number of used wavelengths. In this paper, the ARWA problem is formulated and studied; since ARWA problem is NP-hard, a three-phase genetic algorithm is proposed to solve it. This algorithm is used to find the close-to-optimal solution. Simulated results show that the proposed algorithm is able to achieve good performance.

Over the past few years, a considerable number of studies have been conducted on modeling the bargaining game using artificial agents on within-model interaction. However, very few attempts have been made at study on the interaction and co-evolutionary process among heterogeneous artificial agents. Therefore, we present two kinds of artificial agents, based on genetic algorithm (GA) and reinforcement learning (RL), which play a game on between-model interaction. We investigate their co-evolutionary processes and analyze their parameters using the analysis of variance.

Genetic Algorithms (GA) provide an attractive approach to solving the challenging problem of dynamic routing and wavelength assignment (RWA) in optical Wavelength Division Multiplexing (WDM) networks, because they usually achieve a significantly low blocking probability. Available GA-based dynamic RWA algorithms were designed mainly for WDM networks with a wavelength continuity constraint, and they cannot be applied directly to WDM networks with wavelength conversion capability. Furthermore, the available GA-based dynamic RWA algorithms suffer from the problem of requiring a very time consuming process to generate the first population of routes for a request, which may results in a significantly large delay in path setup. In this paper, we study the dynamic RWA problem in WDM networks with sparse wavelength conversion and propose a novel hybrid algorithm for it based on the combination of mobile agents technique and GA. By keeping a suitable number of mobile agents in the network to cooperatively explore the network states and continuously update the routing tables, the new hybrid algorithm can promptly determine the first population of routes for a new request based on the routing table of its source node, without requiring the time consuming process associated with current GA-based dynamic RWA algorithms. To achieve a good load balance in WDM networks with sparse wavelength conversion, we adopt in our hybrid algorithm a new reproduction scheme and a new fitness function that simultaneously takes into account the path length, number of free wavelengths, and wavelength conversion capability in route selection. Our new hybrid algorithm achieves a better load balance and results in a significantly lower blocking probability than does the Fixed-Alternate routing algorithm, both for optical networks with sparse and full-range wavelength converters and for optical networks with sparse and limited-range wavelength converters. This was verified by an extensive simulation study on the ns-2 network simulator and two typical network topologies. The ability to guarantee both a low blocking probability and a small setup delay makes the new hybrid dynamic RWA algorithm very attractive for current optical circuit switching networks and also for the next generation optical burst switching networks.

In spatial multiplexing systems using multiple antennas, the error-rate performance is heavily dependent on the residual channel estimation error. In this letter, we propose a design method that uses the genetic algorithms to optimize training sequences for accurate channel estimation.

This paper proposes an efficient two-phase optimization approach for a compact W-band double-plane stepped rectangular waveguide filter design, which combines genetic algorithms (GAs) with the simplified transmission-line model and full-wave analysis. Being more efficient and robust than the gradient-based method, the approach can lead to a compact waveguide filter design. Numerical results show that the resultant waveguide filter design with 4 sections (total length 19.6 mm) is sufficient to meet the design goal and provides comparable performance to that with 8 sections (total length 35.6 mm) by the Chebyshev synthesis approach. Based on the present approach, nineteen compact high-pass waveguide filters have been implemented and measured at the W-band with satisfactory performance.

In recent years, there has been an increased focus on the mechanics of information transmission in spiking neural networks. Especially the Noise Shaping properties of these networks and their similarity to Delta-Sigma Modulators has received a lot of attention. However, very little of the research done in this area has focused on the effect the weights in these networks have on the Noise Shaping properties and on post-processing of the network output signal. This paper concerns itself with the various modes of network operation and beneficial as well as detrimental effects which the systematic generation of network weights can effect. Also, a method for post-processing of the spiking output signal is introduced, bringing the output signal more in line with conventional Delta-Sigma Modulators. Relevancy of this research to industrial application of neural nets as building blocks of oversampled A/D converters is shown. Also, further points of contention are listed, which must be thoroughly researched to add to the above mentioned applicability of spiking neural nets.

In this letter, a new computation method for the noise parameters of a linear noisy two-port network is introduced. A new error function, which considers noise figure and source admittance error simultaneously, is proposed to estimate the four noise parameters. The global optimization of the error function is searched directly by using a genetic algorithm.

Personal Communication Network (PCN) is an emerging wireless network that promises many new services for the telecommunication industry. The high speed backbone network (ATM or WDM) is one possible approach to provide broadband wireless transmission with PCN's using the ATM switching networks for interconnection of PCN cells. The wireless ATM backbone network design problem is that of allocating backbone links among ATM switches to reduce the effects of terminal mobility on the performance of ATM-based PCN's. In this paper, the wireless ATM backbone network design (WABND) problem is formulated and studied. The goal of the WABND is to minimize the location update cost under constraints. Since WABND is NP-hard, a heuristic algorithm and a genetic algorithm are proposed to solve it. These algorithms are used to find the close-to-optimal solution. Simulated results show that the proposed algorithms are able to achieve good performance.