Evolving Combinational Logic Circuits Using a Hybrid Quantum Evolution and Particle Swarm Inspired Algorithm

In this paper, an algorithm inspired from quantum evolution and particle swarm to evolve combinational logic circuits is presented. This algorithm uses the framework of the local version of particle swarm optimization with quantum evolutionary algorithms, and integer encoding. A multi-objective fitness function is used to evolve the combinational logic circuits in order obtain feasible circuits with minimal number of gates in the design. A comparative study indicates the superior performance of the hybrid quantum evolution-particle swarm inspired algorithm over the particle swarm and other evolutionary algorithms (such as genetic algorithms) independently

Tunable Neuromimetic Integrated System for Emulating Cortical Neuron Models

Nowadays, many software solutions are currently available for simulating neuron models. Less conventional than software-based systems, hardware-based solutions generally combine digital and analog forms of computation. In previous work, we designed several neuromimetic chips, including the Galway chip that we used for this paper. These silicon neurons are based on the Hodgkin–Huxley formalism and they are optimized for reproducing a large variety of neuron behaviors thanks to tunable parameters. Due to process variation and device mismatch in analog chips, we use a full-custom fitting method in voltage-clamp mode to tune our neuromimetic integrated circuits. By comparing them with experimental electrophysiological data of these cells, we show that the circuits can reproduce the main firing features of cortical cell types. In this paper, we present the experimental measurements of our system which mimic the four most prominent biological cells: fast spiking, regular spiking, intrinsically bursting, and low-threshold spiking neurons into analog neuromimetic integrated circuit dedicated to cortical neuron simulations. This hardware and software platform will allow to improve the hybrid technique, also called “dynamic-clamp,” that consists of connecting artificial and biological neurons to study the function of neuronal circuits

Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms

This paper reproduces the performance of an international market capitalization shipping stock index and two physical shipping indexes by investing only in US stock portfolios. The index-tracking problem is addressed using the differential evolution algorithm and the genetic algorithm. Portfolios are constructed by a subset of stocks picked from the shipping or the Dow Jones Composite Average indexes. To test the performance of the heuristics, three different trading scenarios are examined: annually, quarterly and monthly rebalancing, accounting for transaction costs where necessary. Competing portfolios are also assessed through predictive ability tests. Overall, the proposed investment strategies carry less risk compared to the tracked benchmark indexes while providing investors the opportunity to efficiently replic ate the performance of both the stock and physical shipping indexes in the most cost-effective way

Energy management at municipal parking deck for charging of plug-in hybrid electric vehicles with differential evolution

The development of plug-in hybrid electric vehicles will impact the operation of the power grid since the entrance of these vehicles in substantial numbers will sum to a sizable extra load. This thesis recommends an algorithm for an energy management system (EMS) to apportion constrained power accessible from the utility to a large number of PHEVs parked at a municipal parking deck whereas additionally taking the vehicle battery qualities and client inclination into thought. We start with an itemized portrayal of the framework operation and parts emulated by the proposal of a scientific skeleton for enhancement of power designation. We then recommend the formulation and solution for attaining the ideal assignment methodology taking state of charge augmentation at plug out time as the target. We accomplish by the performance of simulation results. In this thesis, an algorithm is recommended to optimally accomplish a huge number in plug-in hybrid electric vehicles (PHEV's) charging at a municipal parking deck. Differential Evolution optimization is utilized to dispense vitality effectively to the PHEV's. Requirements like vitality value, remaining battery capacity, and staying charging time are utilized. Recreation results are exhibited and talked about

EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION VIA DIFFERENTIAL EVOLUTION

Ph.DDOCTOR OF PHILOSOPH

Algoritmos evolutivos adaptativos para problemas de programação de pessoal

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de ProduçãoA crescente concorrência mundial tem estimulado empresas a tornar seus produtos mais competitivos e serviços mais eficazes, observando a redução de custos. Atualmente, percebe-se um rápido crescimento no setor de serviços, o que mostra a importância da utilização eficaz dos recursos materiais e humanos disponíveis. Com o foco neste crescimento, em special no setor de Call Centers, este trabalho aborda uma metodologia para a resolução de problema de Programação de Pessoal com aplicação em uma empresa neste setor. O problema foi dividido em duas etapas, sendo resolvidas na seguinte ordem: problema de Turnos de Trabalho e problema de Designação dos Turnos aos Atendentes. O primeiro, consiste em eterminar os turnos de trabalho e a quantidade de atendentes em cada turno, de modo a satisfazer à demanda. O segundo, busca a configuração de jornadas de trabalho e a designação destas aos atendentes. Os objetivos são o de minimizar a quantidade de atendentes e de encontrar jornadas que iniciem o turno o mais próximo possível de um horário determinado. Para resolver o problema, foi desenvolvido um Algoritmo Evolutivo (AE) que integra outros AEs, denominado Algoritmo Evolutivo Adaptativo (AEA). A ideia que motivou o desenvolvimento do AEA foi a introdução de um processo que leva em consideração o desempenho prévio de cada AE. Para a resolução do primeiro problema foram utilizados Algoritmos Genéticos, Evolução Diferencial Discreta e o AEA integrando os dois algoritmos anteriores. Também, um modelo de PLI foi desenvolvido e resolvido com os aplicativos XPRESS, Cbc, Gurobi e MOSEK, disponibilizados em um site na internet. Os resultados encontrados pelos AEs se mostraram próximos aos encontrados a partir da resolução do modelo em PLI. Os resultados do AEA e do modelo em PLI foram utilizados como dados de entrada para o segundo problema. Nesta segunda fase foi desenvolvida uma EDD com variáveis mistas (inteiras e binárias). Os resultados encontrados mostraram que para se encontrar resultados adequados para o problema de Programação de Pessoal, não é necessário usar os melhores resultados encontrados na primeira etapa, mas apenas resultados adequados. O AEA desenvolvido pode integrar, além de AEs, outras ferramentas e ser utilizado em outras aplicações. A metodologia adotada pode ser considerada adequada para aplicação em empresas de Call Center, podendo ser expandida para outras com características similares.Increasing global competition has encouraged companies to make their products more competitive and more efficient services, noting the cost savings. Currently, we see a rapid growth in the services sector, what shows the importance of efficient use of available human and material resources. With the focus on this growth, particularly in the Call Center industry, this paper presents a methodology for solving Human Resource problem with an application for a company in this sector. The problem was divided into two phases, resolved in the following order: Working Shift problem and Assignment of the Shifts to the Telephone Operators problem. The first one is to determine the shifts and the number of telephone operators on each shift to meet demand. The second one seeks the setting working hours and the assignment of the telephone operators. The objectives are to minimize the number of telephone operators and find working hours that begin the shift as close as possible to a certain time. To solve the problem has been developed an Evolutionary Algorithm (EA) that integrates other EAs, called Adaptive Evolutionary Algorithm (AEA). The idea that led to the development of the AEA was the introduction of a process that takes into account the previous performance of each EA. To solve the first problem was used Genetic Algorithms, Discrete Differential Evolution and AEA integrating the two previous algorithms. Also, an ILP model was developed and solved with XPRESS, Cbc, Gurobi and MOSEK applications, available on a website. The results find to AEs showed similar to those found from solving the ILP model. The results of AEA and PLI model were used as input data for the second problem. The second phase was developed with an EDD mixed variables (integer and binary). The results showed that in order to find appropriate results for the Human Resource problem, there is no need to use the best results in the first step, but only use the adequate results. The AEA developed may include, beyond the AE, others tools to be used in other applications. The methodology can be considered suitable for application in Call Center companies and can be expanded to others with similar characteristics

Evolutionary Algorithms and Computational Methods for Derivatives Pricing

This work aims to provide novel computational solutions to the problem of derivative pricing. To achieve this, a novel hybrid evolutionary algorithm (EA) based on particle swarm optimisation (PSO) and differential evolution (DE) is introduced and applied, along with various other state-of-the-art variants of PSO and DE, to the problem of calibrating the Heston stochastic volatility model. It is found that state-of-the-art DEs provide excellent calibration performance, and that previous use of rudimentary DEs in the literature undervalued the use of these methods. The use of neural networks with EAs for approximating the solution to derivatives pricing models is next investigated. A set of neural networks are trained from Monte Carlo (MC) simulation data to approximate the closed form solution for European, Asian and American style options. The results are comparable to MC pricing, but with offline evaluation of the price using the neural networks being orders of magnitudes faster and computationally more efficient. Finally, the use of custom hardware for numerical pricing of derivatives is introduced. The solver presented here provides an energy efficient data-flow implementation for pricing derivatives, which has the potential to be incorporated into larger high-speed/low energy trading systems