Ricardo Salem Zebulum

Evolutionary Electronics: Automatic Design of Electronic Circuits and Systems by Genetic Algorithms

From the Publisher: From the explosion of interest, research, and applications of evolutionary computation a new field emerges-evolutionary electronics. Focused on applying evolutionary computation concepts and techniques to the domain of electronics, many researchers now see it as holding the greatest potential for overcoming the drawbacks of conventional design techniques.Evolutionary Electronics: Automatic Design of Electronic Circuits and Systems by Genetic Algorithms formally introduces and defines this area of research, presents its main challenges in electronic design, and explores emerging technologies. It describes the evolutionary computation paradigm and its primary algorithms, and explores topics of current interest, such as multi-objective optimization. The authors examine numerous evolutionary electronics applications, draw conclusions about those applications, and sketch the future of evolutionary computation and its applications in electronics. In coming years, the appearance of more and more advanced technologies will increase the complexity of optimization and synthesis problems, and evolutionary electronics will almost certainly become a key to solving those problems. Evolutionary Electronics is your key to discovering and unlocking the potential of this promising new field.

Comparison of different evolutionary methodologies applied to electronic filter design

We present in this work the application of a set of different evolutionary methodologies in the problem of electronic filter design. The main objectives are to find out which constraints in the filter topologies, if any, must be observed along the evolutionary process and to study the problem of convergence to parsimonious circuits. The new area of evolutionary electronics is introduced, an evolutionary methodology based on variable length representation is presented and the results on the evolution of low-pass and band-pass filters are described.

Analog Circuits Evolution in Extrinsic and Intrinsic Modes

Our work focuses on the use of artificial evolution in Computer Aided Design (CAD) of electronic circuits. Artificial evolution promises to be an important tool for analog CAD development, due to the nature of this task, which has been proven to be much less amenable for standard tools than its digital counterparts. Analog design relies more on the designer’s experience than on systematic rules or procedures. The recent appearance of Field Programmable Analog Arrays (FPAAs) allows evolution to be performed in real silicon, which opens new possibilities to the field. Our work addresses the evolution of amplifiers and oscillators, through the use of a standard simulator and a programmable analog circuit respectively. Furthermore, the issue of the implementability of the circuits evolved in simulation is also examined.

Artificial evolution of active filters: a case study

This article focuses on the application of artificial avolution to the synthesis of analog active filters. The main objective of this research is the achievement of a new class of systems, with advantageous features compared to conventional ones, such as lower power consumption, higher speed and more robustness to noise. The particular problem of designing the amplifier of an AM receiver is examined in this work. Genetic algorithms are employed as our evolutionary tool and two sets of experiments are described. The first set has been carried out using a single objective, the desired frequency response of the circuit. In a second set of experiments, three other objectives have been included in the system. A new multi-objective evaluation methodology was conceived for this second set of experiments. A second approach for evolving active filters, using programmable chips, is also discussed in this paper.

Variable Length Representation in Evolutionary Electronics

This work investigates the application of variable length representation (VLR) evolutionary algorithms (EAs) in the field of Evolutionary Electronics. We propose a number of VLR methodologies that can cope with the main issues of variable length evolutionary systems. These issues include the search for efficient ways of sampling a genome space with varying dimensionalities, the task of balancing accuracy and parsimony of the solutions, and the manipulation of non-coding segments. We compare the performance of three proposed VLR approaches to sample the genome space: Increasing Length Genotypes, Oscillating Length Genotypes, and Uniformly Distributed Initial Population strategies. The advantages of reusing genetic material to replace non-coding segments are also emphasized in this work. It is shown, through examples in both analog and digital electronics, that the variable length genotypes representation is natural to this particular domain of application. A brief discussion on biological genome evolution is also provided.

Evolvable Systems in Hardware Design: Taxonomy, Survey and Applications

This article proposes a taxonomy, presents a survey and describes a set of applications on Evolvable Hardware Systems (EHW). The taxonomy is based on the following properties: Hardware Evaluation Process, Evolutionary Approach, Target Application Area and Evolving Platform. Recent reported applications on EHW are also reviewed, according to the proposed taxonomy. Additionally, a set of digital design applications, developed by the authors are presented. The applications consist in evolving basic digital devices, and the main objective is to evaluate the performance of an EHW system in terms of chromosome representation and evaluation.

PAMA-programmable analog multiplexer array

This paper describes PAMA (Programmable Analog Multiplexer Array), a reconfigurable platform for exploring the intrinsic evolution of analog hardware. The reconfigurable platform consists of integrated circuits whose internal connections can be programmed by the user. The platform characteristics are discussed, such as its flexibility, once the discrete components can be of any type (fine or coarse grained). This latest PAMA version described in this paper presents some advantages concerning the prototype platform which was first used to intrinsically evolve circuits. The Analog Reconfigurable Circuit allows more component terminals, allowing the evolution of a great number of circuits and the circuit evaluation time has decreased considerably.

Synthesis of CMOS operational amplifiers through genetic algorithms

This work studies the problem of CMOS operational amplifiers (OpAmps) design optimisation. The synthesis of these amplifiers can be translated into a multiple-objective optimisation task, in which a large number of specifications have to be taken into account, i.e., GBW area, power consumption and others. We introduce and apply the genetic algorithm (GA) optimisation technique to the proposed problem. A novel multi-objective optimisation methodology is embedded in our genetic algorithm and we focus on the synthesis of a standard analog operational amplifier. The proposed methodology is very general, in the sense that it can be applied to digital and analog VLSI design with multiple-objective specifications.

Short Term Load Forecasting Using Neural Nets

Load forecasting is decisive in the operation of power systems, for economic and security reasons. Many techniques have been proposed in the last two decades [1]. This work presents a short-term load forecasting system (whose main objective is to maintain the generation-load balance) using Neural Networks. Neural Networks have demonstrated to be a very efficient technique to time series forecasting, particularly in load series [2]. In the application shown in this paper, a Neural Network is used to learn the daily load behaviour of a real electrical system (CEMIG, Brazil, 1993). The network inputs are: past load data, the forecasting hour and the type of day (weekday or weekend). The windowing technique [3] is used to identify the series characteristics. Many neural nets with different architectures were tested and the results evaluated in terms of forecasting errors. We achieved an average forecasting error close to 1.5%. The forecasting system was developed in C programming language and includes the pre-processing of the input data, the network training and the forecasting. This system offers to the user options such as: tuning of some network parameters (learning rate, momentum term, number of processors in any layer), usage or not of the forecasted values as network inputs, adjustment of the size of the training window etc.

Evolutionary Systems Applied to the Synthesis of a CPU Controller

Our work introduces an evolutionary approach applied to the design of digital circuits. Particularly, we address the case of synthesising a controller for a simple CPU, a case study which has not been tackled by other authors so far. In order to cope with this problem, a novel circuit evaluation strategy has been employed; and new evolvable hardware systems paradigms derive from this technique. We show that the use of this new evaluation approach allows the achievement of smaller circuits and promises to be effective when the problem scales up. Furthermore, our methodology yields novel digital circuits comparing to conventional design.