We apply evolutionary algorithms to neural network model of associative memory. In the model, some of the appropriate configurations of the synaptic weights allow the network to store a number of patterns as an associative memory. For example, the so-called Hebbian rule prescribes one such configuration. However, if the number of patterns to be stored exceeds a critical amount (over-loaded), the ability to store patterns collapses more or less. Or, synaptic weights chosen at random do not have such an ability. In this paper, we describe a genetic algorithm which successfully evolves both the random synapses and over-loaded Hebbian synapses to function as associative memory by adaptively pruning some of the synaptic connections. Although many authors have shown that the model is robust against pruning a fraction of synaptic connections, improvement of performance by pruning has not been explored, as far as we know.

A reconfiguration method for processor array is proposed in this paper. In the method, genetic algorithm (GA) is used for searching effective spare arrangement, which leads to successful reconfiguration. The effectiveness of the method is demonstrated by computer simulations.

Image restoration using estimated parameters of image model and noise statistics is presented. The image is modeled as the output of a 2-D noncausal autoregressive (NCAR) model. The parameter estimation process is done by using the autocorrelation function and a biased term to a conventional least-squares (LS) method for the noncausal modeling. It is shown that the proposed method gives better results than the other parameter estimation methods which ignore the presence of the noise in the observation data. An appropriate image model selection process is also presented. A genetic algorithm (GA) for solving a multiobjective function with single constraint is discussed.

In this paper, the performances of texture classification based on pyramidal and uniform decomposition are comparatively studied with and without feature selection. This comparison using the subband variance as feature explores the dependence among features. It is shown that the main problem when employing 2-D non-separable wavelet transforms for texture classification is the determination of the suitable features that yields the best classification results. A Max-Max algorithm which is a novel evaluation function based on genetic algorithms is presented to evaluate the classification performance of each subset of selected features. It is shown that the performance with feature selection in which only about half of features are selected is comparable to that without feature selection. Moreover, the discriminatory characteristics of texture spread more in low-pass bands and the features extracted from the pyramidal decomposition are more representative than those from the uniform decomposition. Experimental results have verified the selectivity of the proposed approach and its texture capturing characteristics.

This letter presents a novel variable-rate vector quantizer (VQ) design algorithm, which is a hybrid approach combining a genetic algorithm with the entropy-constrained VQ (ECVQ) algorithm. The proposed technique outperforms the ECVQ algorithm in the sense that it reaches to a nearby global optimum rather than a local one. Simulation results show that, when applied to the image coding, the technique achieves higher PSNR and image quality than those of ECVQ algorithm.

This paper presents an evolutionary scheduling scheme for solving the job shop scheduling problem (JSSP) and other combinatorial optimization problems. The approach is based on a genetized-knowledge genetic algorithm (gkGA). The basic idea behind the gkGA is that knowledge of heuristics which are used in the GA is also encoded as genes alongside the genetic strings, referred to as chromosomes. Furthermore, during the GA selection, weaker heuristics die out while stronger ones survive for a given problem instance. We evaluate our evolutionary scheduling scheme based on the gkGA approach using well known benchmark instances for the JSSP. We observe that the gkGA based scheme is shown to consistently outperform the scheme based on ordinary GAs. In addition the gkGA-based scheme removes the problem of instance dependency.

The increasing activity at millimeter wave frequency band and the growing demand for waveguide components to be applied for integrated circuit purpose have promoted the need for applying the field-theory-based approaches to the design procedure. In this paper, genetic algorithms (GA's) are applied to accurately design the iris-coupled waveguide filters based on network-boundary element method (NBEM). GA's model the natural selection and evolve towards the global optimum, thus avoid being trapped in local minima. Network-boundary element method, which combines boundary element method with network analysis method, derives the network parameters of the guided wave structures with less storage location and central processing unit time. Therefore, NBEM is a feasible and efficient field-theory-based approach for the GA optimization of waveguide filters. With NBEM performing the task of evaluating the performance of the filter designs optimized by the GA, rigorous and optimal designs of the waveguide filters are realized. The obtained analysis and optimization results are compared to a number of reference solutions to demonstrate the validity and accuracy of the proposed approach.

We present experimental results for a structural learning method of feed-forward neural-network classifiers using Principal Component Analysis (PCA) network and Species Genetic Algorithm (SGA). PCA network is used as a means for reducing the number of input units. SGA, a modified GA, is employed for selecting the proper number of hidden units and optimizing the connection links. Experimental results show that the proposed method is a useful tool for choosing an appropriate architecture for high dimensions.

We introduce a concept of regularization into Genetic Algorithms (GAs). Conventional GAs include no explicit regularizing operations. However, the regularization is very effective in solving ill-posed problems. So, we propose a method of regularization to apply GAs to ill-posed problems. This regularization is a kind of consensus operation among neighboring individuals in GAs, and plays the role of `smoothing the solution. ' Our method is based on the evaluation of macroscopic fitness, which is a new fitness criterion. Conventional fitness of an individual in GAs is defined only from the phenotype of the individual, whereas the macroscopic fitness of an individual is evaluated from the phenotypes of the individual and its neighbors. We tested our regularizing operation by means of experiments with an elastic image mapping problem, and showed the effectiveness of the regularization.

In this paper, we propose an efficient and powerful crossover operator in the genetic algorithm for solving the traveling salesman problem (TSP). Our proposed crossover is called the complete subtour exchange crossover (CSEX), and inherits as many good subtours as possible because they are worth preserving for descendants. Before generating the descendants, a prerequisite for the CSEX is that it enumerates all common subtours, which consist of the same set in a pair of subtours on the given two tours of n cities. An algorithm to enumerate all common subtours in the CSEX consumes only O(n) time. In a fundamental experiment, we show the experimental number and length of the common subtours for two randomly generated tours with 5 to 500 thousand elements. In addition, we give the practical behavior of the CSEX and compare the CSEX with a hopeful crossover operator using the benchmark instances for the TSP. Moreover, in another experiment of parallel computing, in order to analyze the performance of the CSEX, we compare the CSEX with hopeful crossovers for 25 benchmarks (48 - 2392 city) using a parallel supercomputer, Paragon. From these results, the CSEX shows an extremely bright performance.

In this paper we consider a parallel and distributed computation of genetic algorithms on loosely-coupled multiprocessor systems. Loosely-coupled ones are more suitable for massively parallel processing and also more easily VLSI implementation than tightly-coupled ones. However, communication overhead on parallel processing is more serious for loosely-coupled ones. We propose in this paper a parallel and distributed execution method of genetic algorithm on loosely-coupled multiprocessor systems of fixed network topologies in which each processor element carries out genetic operations on its own chromosome set and communicates with only the neighbors in order to save communication overhead. We evaluate the proposed method on the multiprocessor systems with ring, torus, and hypercube topologies for benchmark problem instances. From the results, we find that the ring topology is more suitable for the proposed parallel and distributed execution since variety of chromosomes in the ring is kept much more than that in the others. Moreover, we also propose a new network topology called cone which is a hierarchical connection of ring topologies. We show its effectiveness by experimental evaluation.

This paper describes an automatic region segmentation method which is detectable nuclei regions from hematoxylin and eosin (HE)-stained breast tumor images using artificial organisms. In this model, the stained images are treated as virtual environments which consist of nuclei, interstitial tissue and background regions. The movement characteristics of each organism are controlled by the gene and the adaptive behavior of each organism is evaluated by calculating the similarities of the texture features before and after the movement. In the nuclei regions, the artificial organisms can survive, obtain energy and produce offspring. Organisms in other regions lose energy by the movement and die during searching. As a result, nuclei regions are detected by the collective behavior of artificial organisms. The method developed was applied to 9 cases of breast tumor images and detection of nuclei regions by the artificial organisms was successful in all cases. The proposed method has the following advantages: (1) the criteria of each organism's texture feature values (supervised values) for the evaluation of nuclei regions are decided automatically at the learning stage in every input image; (2) the proposed algorithm requires only the similarity ratio as the threshold value when each organism evaluates the environment; (3) this model can successfully detect the nuclei regions without affecting the variance of color tones in stained images which depends on the tissue condition and the degree of malignancy in each breast tumor case.

In this paper, we compare the performance of evolutionary digital filters (EDFs) for IIR adaptive digital filters (ADFs) in terms of convergence behavior and stability, and discuss their advantages. The authors have already proposed the EDF which is controlled by adaptive algorithm based on the evolutionary strategies of living things. This adaptive algorithm of the EDF controls and changes the coefficients of inner digital filters using the cloning method or the mating method. Thus, the adaptive algorithm of the EDF is of a non-gradient and multi-point search type. Numerical examples are given to demonstrate the effectiveness and features of the EDF such that (1) they can work as adaptive filters as expected, (2) they can adopt various error functions such as the mean square error, the absolute sum error, and the maximum error functions, and (3) the EDF using IIR filters (IIR-EDF) has a higher convergence rate and smaller adaptation noise than the LMS adaptive digital filter (LMS-ADF) and the adaptive digital filter based on the simple genetic algorithm (SGA-ADF) on a multiple-peak surface.

In this paper, a new nonlinear integrator with positive phase shifts is proposed. Results of the digital simulation show that the nonlinear integrator has a better performance than the conventional one in a control system.

A genetic algorithm is presented for the blind-deconvolution problem of image restoration without any a priori information about object image or blurring function. The restoration problem is modeled as an optimization problem, whose cost function is to be minimized based on mechanics of natural selection and natural genetics. The applicability of GA for blind-deconvolution problem was demonstrated.

The main defficulty in recognizing 3-D objects from 2-D images is matching 2-D information to the 3-D object representation. The multiple-view approach makes this problem easy to solve by reducing the problem to 2-D to 2-D matching problem. This approach models each 3-D object by a collection of 2-D views from various viewing angles and recognizes an object in the image by finding a 2-D view that has the best match to the image. However, if the size of the model database becomes large, the approach requires long time for the recognition of objects. In this paper we present a 3-D object recognition algorithm based on multiple-view approach. To reduce the recognition time, the proposed algorithm uses the coarse-to-fine process previously proposed by the authors and a genetic algorithm-based search scheme for the selection of a best matched model in the database. And, we could verify from the results of the experiments that the algorithm proposed in this paper is useful to speed up the recognition process in multiple-view based object recognition systems.

Genetic algorithms were introduced by Holland in 1975 as a method of solving difficult optimization problems by means of simulated evolution. A major drawback of genetic algorithms is their slowness when emulated by software on conventional computers. Described is an adaptation of the original genetic algorithm that is advantageous to hardware implementation along with the architecture of a hardware framework that performs the functions of population storage, selection, crossover, mutation, fitness evaluation, and survival determination. Programming of the framework is illustrated with the set coverage problem that exhibits a 6,000 speed-up over software emulation on a 100 MHz workstation.

Genetic algorithms have been shown to be very useful in a variety of search and optimization problems. In this paper we present a genetic algorithm for maximum independent set problem. We adopt a permutation encoding with a greedy decoding to solve the problem. The DIMACS benchmark graphs are used to test our algorithm. For most graphs solutions found by our algorithm are optimal, and there are also a few exceptions that solutions found by the algorithm are almost as large as maximum clique sizes. We also compare our algorithm with a hybrid genetic algorithm, called GMCA, and one of the best existing maximum clique algorithms, called CBH. The exiperimental results show that our algorithm outperformed two of the best approaches by GMCA and CBH in final solutions.

In this paper we discuss a certain constrained optimization problem which is often encountered in the geometrical optimization. Since these kinds of problems occur frequently, constrained genetic optimization becomes very important topic for research. This paper proposes a new methodology to handle constraints using the Genetic Algorithm through a multiprocessor system (FIN) which has a self-similarity network.

In this paper we present an efficient method to solve reachability problems for Petri nets based on genetic algorithms and a kind of random search which is called postpone search. Genetic algorithm is one of algorithms developed for solving several problems of optimization. We apply GAs and postpone search to approximately solving reachability problems. This approach can not determine exact solutions, however, from applicability points of view, does not directly face state space explosion problems and can extend class of Petri nets to deal with very large state space in reasonable time. First we describe how to represent reachability problems on each of GAs and postpone search. We suppose the existence of a nonnegative parickh vector which satisfies the necessary reachability condition. Possible firing sequences of transitions induced by the parickh vector is encoded on GAs. We also define fitness function to solve reachability problems. Reachability problems can be interpreted as an optimization ones on GAs. Next we introduce random reachability problems which are capable of handling state space and the number of firing sequences which enable to reach a target marking from an initial marking. State space and the number of firing sequences are considered as factors which effect on the hardness of reachability problems to solve with stochastic methods. Furthermore, by using those random reachability problems and well known dining philosophers problems as benchmark problems, we compare GAs' performance with the performance of postpone search. Finally we present empirical results that GAa is more useful method than postpone search for solving more harder reachability problems from the both points of view; reliability and efficiency.