Unconstrained evolution of close-to-ideal "LCR" low-pass filter

The unconstrained evolution has already been applied in the past towards design of digital circuits, and extraordinary results have been obtained, including generation of more compact circuits with smaller number of electronic components. In this paper the unconstrained evolution method is developed for analogue circuits. At first, the method is probed on the design of analogue low-pass filter with standard transition band. The algorithm produced the best results in terms of quality of the circuits evolved and evolutionary resources required. Then, the new methodology is applied towards more sophisticated task, the close-to-ideal low-pass filter. The new methodology developed differs from previous ones by its simplicity and represents one of the first attempts to apply evolutionary strategy towards the analogue circuit design. The obtained results are compared in details with low-pass filters previously designed

Digital Filter Design Using Multiobjective Cuckoo Search Algorithm

Digital filters can be divided into finite impulse response (FIR) digital filters and infinite impulse response (IIR) digital filters. Evolutionary algorithms are effective techniques in digital filter designs. One such evolutionary algorithm is Cuckoo Search Algorithm (CSA). The CSA is a heuristic algorithm which emulates a special parasitic hatching habit of some species of cuckoos and have been proved to be an effective method with various applications. This thesis compares CSA with Park-McClellan algorithm on linear-phase FIR Type-1 lowpass, highpass, bandpass and bandstop digital filter design. Furthermore, a multiobjective Cuckoo Search Algorithm (MOCSA) is applied on general FIR digital design with a comparison to Non-dominated Sorting Genetic Algorithm III (NSGA-III). Finally, a constrained multiobjective Cuckoo Search Algorithm is presented and used for IIR digital filter design. The design results of the constrained MOCSA approach compares favorably with other state-of-the-art optimization methods. CSA utilizes Levy flight with wide-range step length for the global walk to assure reaching the global optimum and the approach of local walk to orientate the direction toward the local minima. Furthermore, MOCSA incorporates a method of Euclidean distance combing objective-based equilibrating operations and the searching for the optimal solution into one step and simplifies the procedure of comparison

On comparison of constrained and unconstrained evolutions in analogue electronics on the example of “LC” low-pass filters

The Evolutionary Electronics refers to the design method of electronic circuits with the help of Evolutionary Algorithms. Over the years huge experience has been accumulated and tremendous results have been achieved in this field. Two obvious tendencies are prevailing in the area over designers to improve the performance of Evolutionary Algorithms. First of all, as with any solution-search-algorithm, the designers try to reduce the potential solution space in order to reach the optimum solution faster, putting some constrains onto search algorithm as well as onto potential solutions. At the same time, the second tendency of unconstraining the Evolutionary Algorithms in its search gives unpredictable breakthroughs in results. Enabling the evolution to optimize with more experimental parameters devoted to drive the evolution and adjusted previously manually, is one of an example where such kind of unconstraining takes place. The evolution with the maximum freedom of search can be addressed as unconstrained evolution. The unconstrained evolution has already been applied in the past towards the design of digital circuits, and extraordinary results have been obtained, including generation of circuits with smaller number of electronic components. Recently, the similar method has been introduced by authors of this paper towards the design of analogue circuits. The new algorithm has produced promising results in terms of quality of the circuits evolved and evolutionary resources required. It differed from constrained method by its simplicity and represented one of the first attempts to apply Evolutionary Strategy towards the analogue circuit design. In this paper both conventional constrained evolution and newly developed unconstrained evolution in analogue domain are compared in detail on the example of "LC" low-pass filter design. The unconstrained evolution demonstrates the superior behaviour over the constrained one and exceeds by quality of results the best filter evolved previously by 240%. The experimental results are presented along with detailed analysis. Also, the obtained results are compared in details with low-pass filters designed previously

A VHDL Core for Intrinsic Evolution of Discrete Time Filters with Signal Feedback

The design of an Evolvable Machine VHDL Core is presented, representing a discrete-time processing structure capable of supporting control system applications. This VHDL Core is implemented in an FPGA and is interfaced with an evolutionary algorithm implemented in firmware on a Digital Signal Processor (DSP) to create an evolvable system platform. The salient features of this architecture are presented. The capability to implement IIR filter structures is presented along with the results of the intrinsic evolution of a filter. The robustness of the evolved filter design is tested and its unique characteristics are described

Differential Evolution Particle Swarm Optimization for Digital Filter Design

In this paper, swarm and evolutionary algorithms have been applied for the design of digital filters. Particle swarm optimization (PSO) and differential evolution particle swarm optimization (DEPSO) have been used here for the design of linear phase finite impulse response (FIR) filters. Two different fitness functions have been studied and experimented, each having its own significance. The first study considers a fitness function based on the passband and stopband ripple, while the second study considers a fitness function based on the mean squared error between the actual and the ideal filter response. DEPSO seems to be promising tool for FIR filter design especially in a dynamic environment where filter coefficients have to be adapted and fast convergence is of importance

Optimisation of multiplier-less FIR filter design techniques

This thesis is concerned with the design of multiplier-less (ML) finite impulse response (FIR) digital filters. The use of multiplier-less digital filters results in simplified filtering structures, better throughput rates and higher speed. These characteristics are very desirable in many DSP systems. This thesis concentrates on the design of digital filters with power-of-two coefficients that result in simplified filtering structures. Two distinct classesof ML FIR filter design algorithms are developed and compared with traditional techniques. The first class is based on the sensitivity of filter coefficients to rounding to power-of-two. Novel elements include extending of the algorithm for multiple-bands filters and introducing mean square error as the sensitivity criterion. This improves the performance of the algorithm and reduces the complexity of resulting filtering structures. The second class of filter design algorithms is based on evolutionary techniques, primarily genetic algorithms. Three different algorithms based on genetic algorithm kernel are developed. They include simple genetic algorithm, knowledge-based genetic algorithm and hybrid of genetic algorithm and simulated annealing. Inclusion of the additional knowledge has been found very useful when re-designing filters or refining previous designs. Hybrid techniques are useful when exploring large, N-dimensional searching spaces. Here, the genetic algorithm is used to explore searching space rapidly, followed by fine search using simulated annealing. This approach has been found beneficial for design of high-order filters. Finally, a formula for estimation of the filter length from its specification and complementing both classes of design algorithms, has been evolved using techniques of symbolic regression and genetic programming. Although the evolved formula is very complex and not easily understandable, statistical analysis has shown that it produces more accurate results than traditional Kaiser's formula. In summary, several novel algorithms for the design of multiplier-less digital filters have been developed. They outperform traditional techniques that are used for the design of ML FIR filters and hence contributed to the knowledge in the field of ML FIR filter design

Applications of swarm, evolutionary and quantum algorithms in system identification and digital filter design

The thesis focuses on the application of computational intelligence (CI) techniques for two problems - system identification and digital filter design. In system identification, different case studies have been carried out with equal or reduced number of orders as the original system and also in identifying a blackbox model. Lowpass, Highpass, Bandpass and Bandstop FIR and Lowpass IIR filters have been designed using three algorithms using two different fitness functions. Particle Swarm Optimization (PSO), Differential Evolution based PSO (DEPSO) and PSO with Quantum Infusion (PSO-QI) algorithms have been applied in this work --Abstract, page iii

Open-ended evolution to discover analogue circuits for beyond conventional applications

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s10710-012-9163-8. Copyright @ Springer 2012.Analogue circuits synthesised by means of open-ended evolutionary algorithms often have unconventional designs. However, these circuits are typically highly compact, and the general nature of the evolutionary search methodology allows such designs to be used in many applications. Previous work on the evolutionary design of analogue circuits has focused on circuits that lie well within analogue application domain. In contrast, our paper considers the evolution of analogue circuits that are usually synthesised in digital logic. We have developed four computational circuits, two voltage distributor circuits and a time interval metre circuit. The approach, despite its simplicity, succeeds over the design tasks owing to the employment of substructure reuse and incremental evolution. Our findings expand the range of applications that are considered suitable for evolutionary electronics

Absolutely free extrinsic evolution of passive low-pass filter

Evolutionary electronics is a brunch of evolvable hardware, where the evolutionary algorithm is applied towards electronic circuits. The success of evolutionary search most of all depends on variable length representation methodology. The low-pass filter is a standard task in evolutionary electronics to start with. The results of evolution enable one to qualify whether the methodology is good for further experiments. In this paper the maximum freedom for evolutionary search has been proclaimed as a main target during development of new VLR methodology. The introduction of R-support elements enables to perform an unconstrained evolution of analogue circuits for the first time. The proposed algorithm has been tested on the example of analogue low-pass filter. The experimental results demonstrate that the evolved filter is comparable with filters evolved previously using genetic programming and genetic algorithms techniques. The obtained results are compared in details with low-pass filters previously designed