Investigations into the suitability of artificial neural network for the prediction of rain attenuation based on radio, meteorological and geographical data from ITU-R data bank are presented. First successful steps towards a prediction model of rain attenuation for radio communication based on adaptive learning from the measurement are made. Rain attenuation prediction with the model based on artificial neural network shows good conformity with the measurement. Moreover, a new evolutionary system, EPNet is used to evolve the artificial neural network rain attenuation model obtained both in architecture and weight, and an optimal rain attenuation model with simpler architecture and better prediction accuracy based on EPNet-evolved artificial neural network is obtained. Compared with the ITU-R model, the EPNet-evolved artificial neural network model of rain attenuation proposed in this paper improves the accuracy of rain attenuation prediction and creates a novel way to predict rain attenuation.

The design of artificial neural networks (ANNs) through simulated evolution has been investigated for many years. The use of genetic algorithms (GAs) for such evolution suffers a prominent problem known as the permutation problem or the competing convention problem. This paper proposes a new crossover operator, which we call the selected node crossover (SNX), to overcome the permutation problem of GAs for evolving ANNs. A GA-based evolutionary system (GANet) using the SNX for evolving three layered feedforward ANNs architecture with weight learning is described. GANet uses one crossover and one mutation operators sequentially. If the first operator is successful then the second operator is not applied. GANet is less dependent on user-defined control parameters than the conventional evolutionary methods. GANet is applied to a variety of benchmarks including large (26 class) to small (2 class) classification problems. The results show that GANet can produce compact ANN architectures with small classification errors.

This paper proposes distributed evolutionary digital filters (EDFs) as an improved version of the original EDF. The EDF is an adaptive digital filter which is controlled by adaptive algorithm based on evolutionary computation. In the proposed method, a large population of the original EDF is divided into smaller subpopulations. Each sub-EDF has one subpopulation and executes the small-sized main loop of the original EDF. In addition, the distributed algorithm periodically selects promising individuals from each subpopulation. Then, they migrate to different subpopulations. Numerical examples show that the distributed EDF has a higher convergence rate and smaller steady-state value of the square error than the LMS adaptive digital filter, the adaptive digital filter based on the simple genetic algorithm and the original EDF.

Recognition of specified wave patterns in one-dimensional signals is an important task in many application areas such as computer science, medical science, and geophysics. Many researchers have tried to automate this task with various techniques, recently the soft computing algorithms. This paper proposes a new neuro-fuzzy recognition system for detecting one-dimensional wave patterns using wavelet coefficients as features of the signals and evolution strategy as the training algorithm of the system. The neuro-fuzzy recognition system first trains the wavelet coefficients of the training wave patterns and then evaluates the degree of matching between test wave patterns and the training wave patterns. This system was applied to picking first arrival events in seismic data. Experimental results with three seismic data showed that the system was very successful in terms of learning speed and performances.

Large-scale effects of locally interacting agents are called emergent properties of the system. Emergent properties are often surprising because they can be hard to anticipate the full consequences of even simple forms of interaction. In this paper we address the following questions: how do heterogeneous agents generate emergent coordination, and how do they manage and self-organize macroscopic orders from bottom up without any central authority? These questions will depend crucially on how they interact and adapt their behavior. Agents myopically evolve their behavior based on the threshold rules, which are obtained as the functions of the collective behavior and their idiosyncratic utilities. We obtain the micro-macro dynamics that relate the aggregate behavior with the underlying individual behavior. We show agents' rational behavior combined with the behavior of others produce stable macro behavior, and sometimes unanticipated cyclic behavior. We also consider the roles of conformists and nonconformists to manage emergent macro behavior. As a specific example, we address an emergent and evolutionary approach for designing the efficient network routings.

This paper presents a multi-thread evolutionary programming (MEP) technique that is composed of global, local, and minimal search units. An appropriate search routine is called depending on the current situation and the individuals are updated by using the selected routine. In each search routine, the individuals are updated with a normalized relative fitness function to improve the robustness of the algorithm. The proposed method is applied to the problem of backing up a truck-and-trailer system to a loading dock. A fuzzy logic controller is designed for a truck-and-trailer backer-upper system and the MEP algorithm is used to optimize the representative parameters of the fuzzy logic controller. The simulation results show that the proposed controller performs well even under a large variety of initial positions.

This paper presents an evolutionary technique to build and maintain fault-recoverable digital circuits. As the synthesis of a circuit by genetic algorithm is progressed according to the circuit behavioral objectives and interactions with the environments, the knowledge regarding the architecture as well as the placement and routing processes is not the major concern of the proposed method. The evolutionary behavior of the circuit also prevents the circuit from stuck-at faults by continuously modifying the neighboring circuit blocks accordingly. This is done without the prior knowledge of where and how the faults occur because of the evolutionary nature. Thus, the overhead circuit blocks for fault diagnosis and redundancy are minimized with this design. The fault-recoverable evolvable hardware circuits are synthesized to build a few combinational logics by evolution and the fault recovery capabilities are shown with the reconfigurable FPGA.

This paper presents an efficient graph-based evolutionary optimization technique called Evolutionary Graph Generation (EGG), and its application to the design of fast constant-coefficient multipliers using parallel counter-tree architecture. An important feature of EGG is its capability to handle the general graph structures directly in evolution process instead of encoding the graph structures into indirect representations, such as bit strings and trees. This paper also addresses the major problem of EGG regarding the significant computation time required for verifying the function of generated circuits. To solve this problem, a new functional verification technique for arithmetic circuits is proposed. It is demonstrated that the EGG system can create efficient multiplier structures which are comparable or superior to the known conventional designs.

In a software maintenance phase, since quality assurance engineers frequently only change source codes, the consistency between the source codes and their specification documents cannot be kept. In this paper we propose a supporting technique for changing specification documents automatically so that the specifications can be consistent with the source codes. In our technique, we represent a program with multiple graphs and we consider the changes on programs as the modification of the graphs. The modification of the graphs is formalized with a sequence of the operation on the graphs. We design the rules of how to relate the operations on program graphs to the operations on graphs that represent specification documents. By applying these rules, we can detect what modification and which parts of the specification document should be made to maintain the consistency between the specification and the program, when the program is modified.

Building robots is generally considered difficult, because the designer not only has to predict the interactions between the robot and the environment, but also has to deal with the consequent problems. In recent years, evolutionary algorithms have been proposed to synthesize robot controllers. However, admittedly, it is not satisfactory enough just to evolve the control system, because the performance of the control system depends on other hardware parameters -- the robot body plan -- which might include body size, wheel radius, motor time constant, etc. Therefore, the robot body plan itself should, ideally, also adapt to the task that the evolved robot is expected to accomplish. In this paper, a hybrid GP/GA framework is presented to evolve complete robot systems, including controllers and bodies, to achieve fitness-specified tasks. In order to assess the performance of the developed system, we use it with a fixed robot body plan to evolve controllers for a variety of tasks at first, then to evolve complete robot systems. Experimental results show the promise of our system.

A method to recognize planar objects undergoing affine transformation is proposed in this paper. The method is based upon wavelet multiscale features and Hopfield neural networks. The feature vector consists of the multiscale wavelet transformed extremal evolution. The evolution contains the information of the contour primitives in a multiscale manner, which can be used to discriminate dominant points, hence a good initial state of the Hopfield network can be obtained. Such good initiation enables the network to converge more efficiently. A wavelet normalization scheme was applied to make our method scale invariant and to reduce the distortion resulting from normalizing the object contours. The Hopfield neural network was employed as a global processing mechanism for feature matching and made our method suitable to recognize planar objects whose shape distortion arising from an affine transformation. The Hopfield network was improved to guarantee unique and more stable matching results. A new matching evaluation scheme, which is computationally efficient, was proposed to evaluate the goodness of matching. Two sets of images, noiseless and noisy industrial tools, undergoing affine transformation were used to test the performance of the proposed method. Experimental results showed that our method is not only effective and robust under affine transformation but also can limit the effect of noises.

In pattern recognition using neural networks, it is very difficult for researchers or users to design optimal neural network architecture for a specific task. It is possible for any kinds of neural network architectures to obtain a certain measure of recognition ratio. It is, however, difficult to get an optimal neural network architecture for a specific task analytically in the recognition ratio and effectiveness of training. In this paper, an evolutional method of training and designing feedforward neural networks is proposed. In the proposed method, a neural network is defined as one individual and neural networks whose architectures are same as one species. These networks are evaluated by normalized M. S. E. (Mean Square Error) which presents a performance of a network for training patterns. Then, their architectures evolve according to an evolution rule proposed here. Architectures of neural networks, in other words, species, are evaluated by another measurement of criteria compared with the criteria of individuals. The criteria assess the most superior individual in the species and the speed of evolution of the species. The species are increased or decreased in population size according to the criteria. The evolution rule generates a little bit different architectures of neural network from superior species. The proposed method, therefore, can generate variety of architectures of neural networks. The designing and training neural networks which performs simple 3 3 and 4 4 pixels which include vertical, horizontal and oblique lines classifications and Handwritten KATAKANA recognitions are presented. The efficiency of proposed method is also discussed.

An evolutionary algorithm is used to evolve a digital circuit which computes a simple hash function mapping a 16-bit address space into an 8-bit one. The target technology is FPGA, where the search space of the algorithm is made of the combinational functions computed by cells and of the interconnections among cells. The evolutionary technique has been applied to five different interconnection topologies, specified by neighbourhood graphs. This circuit is readily applicable to the design of set-associative cache memories. Possible use of the evolutionary approach presented in the paper for on-line tuning of the function during cache operation is also discussed.

This paper presents a new approach to designing arithmetic circuits by using a graph-based evolutionary optimization technique called Evolutionary Graph Generation (EGG). The key idea of the proposed method is to introduce a higher level of abstraction for arithmetic algorithms, in which arithmetic circuit structures are modeled as data-flow graphs associated with specific number representation systems. The EGG system employs evolutionary operations to transform the structure of graphs directly, which makes it possible to generate the desired circuit structure efficiently. The potential capability of EGG is demonstrated through an experiment of generating constant-coefficient multipliers.

This paper presents a timing-driven global routing algorithm based on coarse pin assignment, block reshaping, and positioning for VLSI building block layout. As opposed to conventional approaches, we combine pin assignment and global routing problems into one problem. The proposed algorithm determines global routes, coarse pin assignments, and block shapes and positions so as to minimize the chip area and total wire length of nets under the given timing constraints. It is based on an iterative improvement paradigm and performs rip-up and rerouting, block reshaping, and positioning in the manner of simulated evolution taking shapes of soft blocks and routing congestion into consideration until the solution is not further improved. The Elmore delay model is adopted for the interconnection delay model. Experimental results show the effectiveness of the proposed algorithm.

This paper proposes an automatic structural programming system. Genetic Programming achieves success for automatic programming using the evolutionary process. However, the approach doesn't deal with the essential program concept in the sense of what is called a program in software science. It is useful that a program be structured by various sub-structures, i. e. subroutines, however, the above-mentioned approach treats a single program as one sequence. As a result of the above problem, there is a lack of reusability, flexibility, and a decreases in the possibility of use as a utilitarian programming system. In order to realize a structural programming system, this paper proposes a method which can generate a program constructed by subroutines, named formula, using the evolutionary process.

The problem of genetic algorithm's efficiency has been attracting the attention of genetic algorithm community. Over the last decade, considerable researches have focused on improving genetic algorithm's performance. However, they are generally under the framework of natural evolutionary mechanism and the major genetic operators, crossover and mutation, are activated by the prior probabilities. An operator based on a prior probability possesses randomness, that is, the unexpected individuals are frequently operated, but the expected individuals are sometimes not operated. Moreover, as the evaluation function is the link between the genetic algorithm and the problem to be solved, the evaluation function provides the heuristic information for evolutionary search. Therefore, how to use this kind of heuristic information (present and past) is influential in the efficiency of evolutionary search. This paper, as an attempt, presents a eugenics-based genetic algorithm (EGA) -- a genetic algorithm that reflects the human's decision will (eugenics), and fully utilizes the heuristic information provided by the evaluation function for the decisions. In other words, EGA = evolutionary mechanisms + human's decision will + heuristic information. In EGA, the ideas of the positive eugenics and the negative eugenics are applied as the principle of selections and the selections are not activated by the prior probabilities but by the evaluation values of individuals. A method of genealogical chain-based selection for mutation is proposed, which avoids the blindness of stochastic mutation and the disruptive problem of mutation. A control strategy of reasonable competitions is proposed, which brings the effects of crossover and mutation into full play. Three examples, the minimum problem of a standard optimizing function--De Jong's test function F2, a typical combinatorial optimization problem--the traveling salesman problem, and a problem of identifying nonlinear system, are given to show the good performance of EGA.

This letter proposes evolutionary digital filters (EDFs) as new adaptive digital filters. The EDF is an adaptive filter which is controlled by adaptive algorithm based on the evolutionary strategies of living things. It consists of many linear/time-variant inner digital filters which correspond to individuals. The adaptive algorithm of the EDF controls and changes the coefficients of inner filters using the cloning method (the asexual reproduction method) or the mating method (the sexual reproduction method). Thus, the search algorithm of the EDF is a non-gradient and multi-point search algorithm. Numerical examples are given to show the effectiveness and features of the EDF such that they are not susceptible to local minimum in the multiple-peak performance surface.

This paper describes the design of the reflective concurrent object-oriented specification language RMondel. RMondel is designed for the specification and modeling of distributed systems. It allows the development of executable specifications which may be modified dynamically. Reflection in RMondel is supported by two fundamental features that are: Structural Reflection (SR) and Behavioral Reflection (BR). Reflection is the capability to monitor and modify dynamically the structure and the behavior of the system. We show how the features of the language are enhanced using specific meta-operations and meta-objects, to allow for the dynamic modification of types (classes) and instances using the same language. RMondel specification can be modified by adding or modifying types and instances to get a new adapted specification. Consistency is checked dynamically at the type level as well as at the specification level. At the type level, structural and behavioral constrations are defined to preserve the conformance of types. At the specification level, a transaction mechanism and a locking protocol are defined to ensure the consistency of the whole specification.

We discuss properties of acyclic graph evolution driven by node-firing. The research background and basic concepts of acyclic graph evolution are from the mutual exclusion problem in distributed environments. We proposed in our previous work a mutual exclusion protocol which is based on the notion of evolution trajectories of acyclic graphs. In this paper, we analyze firing concurrency and periodicity of the acyclic graph evolution, from graph theoretical point of views, and investigate topological conditions for assuring the number of firable nodes below a some fixed constant, at any instance of the evolution trajectory. A marked graph, a subclass of Petri nets, is often utilized as a proof tool in analysis.