Evolution of Impulse Bursts Noise Filters

Evolutionary algorithms (EAs) are population-based search algorithms that have been successfully applied to solve hard optimization problems in many application domains. Since the early 1990's researchers have begun to apply evolutionary algorithms to synthesize electronic circuits. Nowadays it is evident that the evolutionary design approach can automatically create efficient electronic circuits in many domains. In this tutorial, fundamental concepts of evolutionary design of digital circuits are presented. In particular, the tutorial deals with Cartesian Genetic Programming (CGP) — a method of genetic programming that in many cases outperforms conventional synthesis tools in terms of achievable circuit size reduction. Innovative designs will be presented in domains of small combinational circuits (where the goal is to minimize the number of gates), middle-size circuits (such as image filters intended for FPGAs where the goal is to obtain the quality of filtering of conventional methods for a significantly lower cost on a chip) and large circuits (such as benchmark circuits for comparison of testability analysis methods), covering thus circuit complexity from a few gates to millions of gates. For example, one of evolved image filters is now protected by utility model in the Czech Republic (patent pending). Evolved circuits will be compared with the best-known conventional designs. We will also show how to deal with the so-called scalability problems of evolutionary design which have been identified as the most important problems from the point of view of practical applications. In summary, tutorial participants will become familiar with the state of the art methods in the area of digital circuit evolution. They will learn how to apply CGP, construct the fitness function and run experiments.

In this paper we show how information theory concepts can be used in evolutionary circuit design and minimization problems. Conditional entropy, mutual information, and normalized mutual information are commonly used to measure or estimate the amount of information shared by two random variables. Although the simple number reported by these measures may guide the evolutionary search, we show that normalized mutual information produces more amenable fitness landscape for search than the others. Several landscape plots and experiments are used to support and explain our main argument.

Evolutionary circuit design for fast FPGA-based classification of network application protocols

Evolutionary optimization algorithms have been recently applied to optimal digital IIR filter design. In this paper, we apply a Bandwidth Adaptive Harmony Search (BAHS) algorithm to the design of 1-dimensional IIR filters. Harmony Search is an evolutionary algorithm, which emulates the improvisation process of musicians. We have modified the algorithm by setting the bandwidth equal to the standard deviation of the vectors in the harmony memory, and by introducing a penalty function to ensure IIR filter stability. The proposed variant has been applied to the design of low-pass, high-pass, band pass and band stop filters, by minimizing the L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> norm approximation errors and the maximum pass band and stop band ripples. The algorithm has been compared with the TIA approach. The harmony search variant has been found to outperform the TIA approach in designing IIR filters having near-desired frequency responses.

Variety of real-world optimization problems can be successfully solved by employing a powerful technique, called Differential Evolution (DE) algorithm. The popularity of DE has grown tremendously since its inception as it includes a very few number of control parameters. However, the selection or tuning of these parameters plays a crucial role in determining the performance of the algorithm in terms of its convergence behaviour. In this paper, a novel Self-Adaptive DE (SADE) approach has been proposed for the de sign of a multiplier-less low-pass linear-phase FIR filter to improve the computational efficiency of the algorithm. For this purpose, the convergence behaviour of the SADE technique has been presented and it has been compared with that of traditional DE technique. Additionally, the performance of the SADE-optimized filter has been evaluated in terms of its magnitude response. The corresponding magnitude response for the DE-optimized filter has also been presented for comparison. It has been established that the proposed SADE algorithm outperforms the traditional DEfor this particular design problem.

A combinational circuit design method that can be completely implemented on a FPGA is presented. For carrying out faster evolution in evolutionary circuit design, a sub population based MOEA (multiobjective evolutionary algorithm) is employed in which the reconfigurable circuit (RC) architecture is encoded by Cartesian Genetic Programming (CGP). For hardware implementation, the Celoxica RC1000 PCI is employed which includes Xilinx Virtex xcv 2000E FPGA chip. This PCI card is communicating with host PC and acting as an evolvable platform. MOEA adopted modules are designed into a FPGA chip for discussing the rationality of circuit design method. Results of direct evolution and results of incremental evolution is compared, it shows MOEA is most efficient in the aspect of speeding up the convergence of evolution.

For problems involving uncertainties in design variables and parameters, a bi-objective evolutionary algorithm (EA) based approach to design optimization using evidence theory is proposed and implemented in this paper. In addition to a functional objective, a plausibility measure of failure of constraint satisfaction is minimized. Despite some interests in classical optimization literature, this is the first attempt to use evidence theory with an EA. Due to EA's flexibility in modifying its operators, nonrequirement of any gradient, its ability to handle multiple conflicting objectives, and ease of parallelization, evidence-based design optimization using an EA is promising. Results on a test problem and two engineering design problems show that the modified evolutionary multi-objective optimization algorithm is capable of finding a widely distributed trade-off frontier showing different optimal solutions corresponding to different levels of plausibility failure limits. Furthermore, a single-objective evidence-based EA is found to produce better optimal solutions than a previously reported classical optimization algorithm. Furthermore, the use of a graphical processing unit (GPU) based parallel computing platform demonstrates EA's performance enhancement around 160–700 times in implementing plausibility computations. Handling uncertainties of different types are getting increasingly popular in applied optimization studies and this EA based study is promising to be applied in real-world design optimization problems.

An innovative method for the automatic design of optical systems is presented and verified. The proposed method is based on a multi-objective evolutionary memetic optimization algorithm. The multi-objective approach simultaneously, but separately, addresses the image quality, tolerance, and complexity of the system. The memetic technique breaks down the search for optical designs in to three different parts or phases: optical glass selection, exploration, and exploitation. The optical glass selection phase defines the most appropriate set of glasses for the system under design. The glass selection phase limits the available glasses from hundreds to just a few, drastically reducing the design space and significantly increasing the efficiency of the automatic design method. The exploration phase is based on an evolutionary algorithm (EA), more specifically, on a problem-tailored generalized extremal optimization (GEO) algorithm, named Optical GEO (O-GEO). The new EA incorporates many features customized for lens design, such as optical system codification and diversity operators. The trade-off systems found in the exploration phase are refined by a local search, based on the damped least square method in the exploitation phase. As a result, the method returns a set of trade-off solutions, generating a Pareto front. Our method delivers alternative and useful insights for the compromise solutions in a lens design problem. The efficiency of the proposed method is verified through real-world examples, showing excellent results for the tested problems.

Design of multipurpose digital FIR double-band filter using hybrid firefly differential evolution algorithm

The design as a process is not a new topic in architecture, yet some theories are widely unexplored, such as the multi-stage decision-making (MD) process. This design method provides multiple solutions for one design problem and is characterized by design stages. By adding new building components in every stage, multiple solutions are created for each design solution from the previous stage. If the MD process is to be applied in architectural practice, fundamental and theoretical knowledge about it becomes necessary. This paper investigates the impact of sequence of design stages on the design solutions in the MD process. A basic case study provides the necessary data for comparing different sequences and gaining fundamental knowledge of the MD process. The study contains a parametric model for building generation, a parametric Life Cycle Assessment tool and an optimization mechanism based on Evolutionary Algorithms.

The design of p-cycles in WDM mesh networks has been extensively studied. However, most of studies only deal with a single link failure rather than node failure. In this paper, we develop a new scalable and efficient design method for computing node-protecting p-cycles. The performance of our new proposed design method makes an obvious improvement 20% in capacity redundancy over that of the previous design method. The conventional design methods formulate the problem of pcycle design as an Integer Linear Program (ILP). To solve the ILP, the prerequisite is to a priori enumerated all possible pcycle candidates. For a large network, the resulting ILP maybe intractable as the huge number of cycles may exist. We propose a new design and solution method based on large scale optimization tools, namely Column Generation (CG), where pcycles are generated on-line when needed. The main advantage of our CG-based method is that no p-cycles are a-priori off-line enumerated, the generation of the promising set of p-cycles is embedded in the optimization process. Extensive experiments on several network instances have been conducted for comparison. Experimental results show that our new proposed methods outperforms the previous method in terms of capacity efficiency.

In this paper a state of the art, main problems and future trends concerning evolutionary design of combinational digital circuits are presented. Several evolutionary algorithms for design and optimization of combinational digital circuits are also shortly described. In evolutionary design methods the main problem is related to "combinatorial explosion" existing in those methods. Because evolutionary design methods are based on "generate and test" model, therefore together with growing of digital circuit input number, the number of potential combinations which must be tested are growing exponentially. This problem is a "bottle-neck" of those methods, and therefore it is discussed in details in this paper. To solve this problem it becomes important to elaborate efficient decomposition techniques of the designed circuits to some less complex sub-circuits, and then to design each of them independently. Also, it is important to create novel genetic operators (as mutation and crossover) which will always lead to acceptable solutions (solutions which satisfy the truth table). Due to such kind of operators, any repair mechanisms need not necessarily be used, and therefore the "generate and test" model will be simplified only to generation of new solutions, and therefore evolutionary design methods will become more effective.

Optimal design of shunt active power filter for power quality enhancement using predator-prey based firefly optimization

Image processing has an important role in signal processing. The occurrence of noise in the image can degrade its quality and can cause loss of information. In this paper, design of Gaussian, mean, and median filters is considered. Gaussian and mean filters are linear filters and are designed using convolution between 3 × 3 image pixel matrix and kernel matrix. The median filter is a nonlinear filter. An SRAM-based FPGA implementation of such a filter is susceptible to memory bit flips that are caused by single-event upsets (SEUs). Hence, a protection method is needed to ensure proper working of the median filter. Here, the median filter design proposed in (Aranda et al. in J. IEEE Transactions on Nuclear Science 64:2219–2226, 2017 [7]) is used to check if the median value obtained is within a dynamic range. The performance of these three filters is evaluated by considering image quality metrics such as PSNR and correlation coefficient with four different noises. The results show choice of the filter for denoising, based on the type of noise present in the image. In recent times, FPGA technology has become a reliable method for implementing image processing algorithms due to their fast response compared to image processing in MATLAB. The noisy image to pixel conversion is done in MATLAB. The 3 × 3 sliding window approach is used for filter coding in Verilog, and the FPGA synthesis on virtex5 is done with Xilinx ISE 14.5. The images are reconstructed with filtered pixels in MATLAB.

Neuro-genetic systems, a particular type of evolving systems, have become a very important topic of study in evolutionary design. They are biologically-inspired computational models that use evolutionary algorithms (EAs) in conjunction with neural networks (NNs) to solve problems. EAs are based on natural genetic evolution of individuals in a defined environment and they are useful for complex optimization problems with huge number of parameters and where the analytical solutions are difficult to obtain.