Plataforma de desenvolvimento de circuitos eletrônicos adaptativos.

Este trabalho apresenta uma arquitetura geral para evolução de circuitos eletrônicos analógicos baseada em algoritmos genéticos. A organização lógica privilegia a interoperabilidade de seus principais componentes, incluindo a possibilidade de substituição ou melhorias internas de suas funcionalidades. A plataforma implementada utiliza evolução extrínseca, isto é, baseada em simulação de circuitos, e visa facilidade e flexibilidade para experimentação. Ela viabiliza a interconexão de diversos componentes aos nós de um circuito eletrônico que será sintetizado ou adaptado. A técnica de Algoritmos Genéticos é usada para buscar a melhor forma de interconectar os componentes para implementar a função desejada. Esta versão da plataforma utiliza o ambiente MATLAB com um toolbox de Algoritmos Genéticos e o PSpice como simulador de circuitos. Os estudos de caso realizados apresentaram resultados que demonstram a potencialidade da plataforma no desenvolvimento de circuitos eletrônicos adaptativos.

Síntese evolucionária de circuitos sequenciais inspirada nos princípios da computação quântica.

Esta dissertação investiga a aplicação dos algoritmos evolucionários inspirados na computação quântica na síntese de circuitos sequenciais. Os sistemas digitais sequenciais representam uma classe de circuitos que é capaz de executar operações em uma determinada sequência. Nos circuitos sequenciais, os valores dos sinais de saída dependem não só dos valores dos sinais de entrada como também do estado atual do sistema. Os requisitos cada vez mais exigentes quanto à funcionalidade e ao desempenho dos sistemas digitais exigem projetos cada vez mais eficientes. O projeto destes circuitos, quando executado de forma manual, se tornou demorado e, com isso, a importância das ferramentas para a síntese automática de circuitos cresceu rapidamente. Estas ferramentas conhecidas como ECAD (Electronic Computer-Aided Design) são programas de computador normalmente baseados em heurísticas. Recentemente, os algoritmos evolucionários também começaram a ser utilizados como base para as ferramentas ECAD. Estas aplicações são referenciadas na literatura como eletrônica evolucionária. Os algoritmos mais comumente utilizados na eletrônica evolucionária são os algoritmos genéticos e a programação genética. Este trabalho apresenta um estudo da aplicação dos algoritmos evolucionários inspirados na computação quântica como uma ferramenta para a síntese automática de circuitos sequenciais. Esta classe de algoritmos utiliza os princípios da computação quântica para melhorar o desempenho dos algoritmos evolucionários. Tradicionalmente, o projeto dos circuitos sequenciais é dividido em cinco etapas principais: (i) Especificação da máquina de estados; (ii) Redução de estados; (iii) Atribuição de estados; (iv) Síntese da lógica de controle e (v) Implementação da máquina de estados. O Algoritmo Evolucionário Inspirado na Computação Quântica (AEICQ) proposto neste trabalho é utilizado na etapa de atribuição de estados. A escolha de uma atribuição de estados ótima é tratada na literatura como um problema ainda sem solução. A atribuição de estados escolhida para uma determinada máquina de estados tem um impacto direto na complexidade da sua lógica de controle. Os resultados mostram que as atribuições de estados obtidas pelo AEICQ de fato conduzem à implementação de circuitos de menor complexidade quando comparados com os circuitos gerados a partir de atribuições obtidas por outros métodos. O AEICQ e utilizado também na etapa de síntese da lógica de controle das máquinas de estados. Os circuitos evoluídos pelo AEICQ são otimizados segundo a área ocupada e o atraso de propagação. Estes circuitos são compatíveis com os circuitos obtidos por outros métodos e em alguns casos até mesmo superior em termos de área e de desempenho, sugerindo que existe um potencial de aplicação desta classe de algoritmos no projeto de circuitos eletrônicos.

Supply Chain Management practices in the electro-electronics sector in Brazil: Evolutionary approach, information technology adoption and management actions

This paper examines the current level of adoption of Supply Chain Management (SCM) practices in the electro-electronic sector in Brazil and aims to identify the management and Information Technology (IT) actions that have been implemented to support the adoption of those practices. An e-mail survey was conducted. Descriptive statistics techniques were employed for data analysis. This study makes contributions to the electro-electronics sector and to the topics related to SCM, such as identifi cation and level of adoption of SCM practices. Another contribution of this research is the investigation of whether approaches such as Enterprise Resources Planning (ERP), Workshop with Customers, Electronic Data Interchange (EDI), Workshop with Suppliers and electronic Kanban are commonly used to support SCM practices. So far, this is the fi rst research on SCM practices in the electro-electronics sector in Brazil. Copyright © 2012 Inderscience Enterprises Ltd.

FUELGEN: effective evolutionary design of refuellings for pressurized water reactors

The paper describes the design of an efficient and robust genetic algorithm for the nuclear fuel loading problem (i.e., refuellings: the in-core fuel management problem) - a complex combinatorial, multimodal optimisation., Evolutionary computation as performed by FUELGEN replaces heuristic search of the kind performed by the FUELCON expert system (CAI 12/4), to solve the same problem. In contrast to the traditional genetic algorithm which makes strong requirements on the representation used and its parameter setting in order to be efficient, the results of recent research results on new, robust genetic algorithms show that representations unsuitable for the traditional genetic algorithm can still be used to good effect with little parameter adjustment. The representation presented here is a simple symbolic one with no linkage attributes, making the genetic algorithm particularly easy to apply to fuel loading problems with differing core structures and assembly inventories. A nonlinear fitness function has been constructed to direct the search efficiently in the presence of the many local optima that result from the constraint on solutions.

Femtosecond pulse shaping using differential evolutionary algorithm and wavelet operators

Evolutionary meta-algorithms for pulse shaping of broadband femtosecond duration laser pulses are proposed. The genetic algorithm searching the evolutionary landscape for desired pulse shapes consists of a population of waveforms (genes), each made from two concatenated vectors, specifying phases and magnitudes, respectively, over a range of frequencies. Frequency domain operators such as mutation, two-point crossover average crossover, polynomial phase mutation, creep and three-point smoothing as well as a time-domain crossover are combined to produce fitter offsprings at each iteration step. The algorithm applies roulette wheel selection; elitists and linear fitness scaling to the gene population. A differential evolution (DE) operator that provides a source of directed mutation and new wavelet operators are proposed. Using properly tuned parameters for DE, the meta-algorithm is used to solve a waveform matching problem. Tuning allows either a greedy directed search near the best known solution or a robust search across the entire parameter space.

New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Multi Objective Evolutionary Algorithm Applied to the Optimal Power Flow Problem

This work presents the application of a multiobjective evolutionary algorithm (MOEA) for optimal power flow (OPF) solution. The OPF is modeled as a constrained nonlinear optimization problem, non-convex of large-scale, with continuous and discrete variables. The violated inequality constraints are treated as objective function of the problem. This strategy allows attending the physical and operational restrictions without compromise the quality of the found solutions. The developed MOEA is based on the theory of Pareto and employs a diversity-preserving mechanism to overcome the premature convergence of algorithm and local optimal solutions. Fuzzy set theory is employed to extract the best compromises of the Pareto set. Results for the IEEE-30, RTS-96 and IEEE-354 test systems are presents to validate the efficiency of proposed model and solution technique.

Multiarea optimal power flow using multiobjective evolutionary algorithm

In this work the multiarea optimal power flow (OPF) problem is decoupled into areas creating a set of regional OPF subproblems. The objective is to solve the optimal dispatch of active and reactive power for a determined area, without interfering in the neighboring areas. The regional OPF subproblems are modeled as a large-scale nonlinear constrained optimization problem, with both continuous and discrete variables. Constraints violated are handled as objective functions of the problem. In this way the original problem is converted to a multiobjective optimization problem, and a specifically-designed multiobjective evolutionary algorithm is proposed for solving the regional OPF subproblems. The proposed approach has been examined and tested on the RTS-96 and IEEE 354-bus test systems. Good quality suboptimal solutions were obtained, proving the effectiveness and robustness of the proposed approach. ©2009 IEEE.

Optimized dynamic response of parallel operation of two single phase inverters based on evolutionary theory

In this work it is proposed an optimized dynamic response of parallel operation of two single-phase inverters with no control communication. The optimization aims the tuning of the slopes of P-ω and Q-V curves so that the system is stable, damped and minimum settling time. The slopes are tuned using an algorithm based on evolutionary theory. Simulation and experimental results are presented to prove the feasibility of the proposed approach. © 2010 IEEE.

Power flow optimization for grid connected inverter using evolutionary algorithm and additional control loop

In this work it is proposed to validate an evolutionary tuning algorithm in plants composed by a grid connected inverter. The optimization aims the tuning of the slopes of P-Ω and Q-V curves so that the system is stable, damped and minimum settling time. Simulation and experimental results are presented to prove the feasibility of the proposed approach. However, experimental results demonstrate a compromising effect of grid frequency oscillations in the active power transferring. In addition, it was proposed an additional loop to compensate this effect ensuring a constant active power flow. © 2011 IEEE.

Efficient Placement of Fault Indicators in an Actual Distribution System Using Evolutionary Computing

This paper proposes an evolutionary computing strategy to solve the problem of fault indicator (FI) placement in primary distribution feeders. More specifically, a genetic algorithm (GA) is employed to search for an efficient configuration of FIs, located at the best positions on the main feeder of a real-life distribution system. Thus, the problem is modeled as one of optimization, aimed at improving the distribution reliability indices, while, at the same time, finding the least expensive solution. Based on actual data, the results confirm the efficiency of the GA approach to the FI placement problem.

A survey of evolutionary algorithms for decision-tree induction

This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.

New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection

Although nontechnical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy and to characterize possible illegal consumers has not attracted much attention in this context. In this paper, we focus on this problem by reviewing three evolutionary-based techniques for feature selection, and we also introduce one of them in this context. The results demonstrated that selecting the most representative features can improve a lot of the classification accuracy of possible frauds in datasets composed by industrial and commercial profiles.