Modelo de otimização multiobjetivo aplicado ao projeto de concepção de submarinos convencionais.

Este trabalho apresenta um modelo de otimização multiobjetivo aplicado ao projeto de concepção de submarinos convencionais (i.e. de propulsão dieselelétrica). Um modelo de síntese que permite a estimativa de pesos, volume, velocidade, carga elétrica e outras características de interesse para a o projeto de concepção é formulado. O modelo de síntese é integrado a um modelo de otimização multiobjetivo baseado em algoritmos genéticos (especificamente, o algoritmo NSGA II). A otimização multiobjetivo consiste na maximização da efetividade militar do submarino e na minimização de seu custo. A efetividade militar do submarino é representada por uma Medida Geral de Efetividade (OMOE) estabelecida por meio do Processo Analítico Hierárquico (AHP). O Custo Básico de Construção (BCC) do submarino é estimado a partir dos seus grupos de peso. Ao fim do processo de otimização, é estabelecida uma Fronteira de Pareto composta por soluções não dominadas. Uma dessas soluções é selecionada para refinamento preliminar e os resultados são discutidos. Subsidiariamente, esta dissertação apresenta discussão sucinta sobre aspectos históricos e operativos relacionados a submarinos, bem como sobre sua metodologia de projeto. Alguns conceitos de Arquitetura Naval, aplicada ao projeto dessas embarcações, são também abordados.

FPGA implementation of a multi-population PBIL algorithm

Evolutionary-based algorithms play an important role in finding solutions to many problems that are not solved by classical methods, and particularly so for those cases where solutions lie within extreme non-convex multidimensional spaces. The intrinsic parallel structure of evolutionary algorithms are amenable to the simultaneous testing of multiple solutions; this has proved essential to the circumvention of local optima, and such robustness comes with high computational overhead, though custom digital processor use may reduce this cost. This paper presents a new implementation of an old, and almost forgotten, evolutionary algorithm: the population-based incremental learning method. We show that the structure of this algorithm is well suited to implementation within programmable logic, as compared with contemporary genetic algorithms. Further, the inherent concurrency of our FPGA implementation facilitates the integration and testing of micro-populations.

A genetic estimation algorithm for parameters of stochastic ordinary differential equations

A generic method for the estimation of parameters for Stochastic Ordinary Differential Equations (SODEs) is introduced and developed. This algorithm, called the GePERs method, utilises a genetic optimisation algorithm to minimise a stochastic objective function based on the Kolmogorov-Smirnov statistic. Numerical simulations are utilised to form the KS statistic. Further, the examination of some of the factors that improve the precision of the estimates is conducted. This method is used to estimate parameters of diffusion equations and jump-diffusion equations. It is also applied to the problem of model selection for the Queensland electricity market. (C) 2003 Elsevier B.V. All rights reserved.

Time, Cost, and Environmental Impact Analysis for Sustainable Design at Multiple Building Levels

Construction projects are complex endeavors that require the involvement of different professional disciplines in order to meet various project objectives that are often conflicting. The level of complexity and the multi-objective nature of construction projects lend themselves to collaborative design and construction such as integrated project delivery (IPD), in which relevant disciplines work together during project conception, design and construction. Traditionally, the main objectives of construction projects have been to build in the least amount of time with the lowest cost possible, thus the inherent and well-established relationship between cost and time has been the focus of many studies. The importance of being able to effectively model relationships among multiple objectives in building construction has been emphasized in a wide range of research. In general, the trade-off relationship between time and cost is well understood and there is ample research on the subject. However, despite sustainable building designs, relationships between time and environmental impact, as well as cost and environmental impact, have not been fully investigated. The objectives of this research were mainly to analyze and identify relationships of time, cost, and environmental impact, in terms of CO2 emissions, at different levels of a building: material level, component level, and building level, at the pre-use phase, including manufacturing and construction, and the relationships of life cycle cost and life cycle CO2 emissions at the usage phase. Additionally, this research aimed to develop a robust simulation-based multi-objective decision-support tool, called SimulEICon, which took construction data uncertainty into account, and was capable of incorporating life cycle assessment information to the decision-making process. The findings of this research supported the trade-off relationship between time and cost at different building levels. Moreover, the time and CO2 emissions relationship presented trade-off behavior at the pre-use phase. The results of the relationship between cost and CO2 emissions were interestingly proportional at the pre-use phase. The same pattern continually presented after the construction to the usage phase. Understanding the relationships between those objectives is a key in successfully planning and designing environmentally sustainable construction projects.

Parallel Problem Solving from Nature – PPSN XIV:14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings

This book constitutes the refereed proceedings of the 14th International Conference on Parallel Problem Solving from Nature, PPSN 2016, held in Edinburgh, UK, in September 2016. The total of 93 revised full papers were carefully reviewed and selected from 224 submissions. The meeting began with four workshops which offered an ideal opportunity to explore specific topics in intelligent transportation Workshop, landscape-aware heuristic search, natural computing in scheduling and timetabling, and advances in multi-modal optimization. PPSN XIV also included sixteen free tutorials to give us all the opportunity to learn about new aspects: gray box optimization in theory; theory of evolutionary computation; graph-based and cartesian genetic programming; theory of parallel evolutionary algorithms; promoting diversity in evolutionary optimization: why and how; evolutionary multi-objective optimization; intelligent systems for smart cities; advances on multi-modal optimization; evolutionary computation in cryptography; evolutionary robotics - a practical guide to experiment with real hardware; evolutionary algorithms and hyper-heuristics; a bridge between optimization over manifolds and evolutionary computation; implementing evolutionary algorithms in the cloud; the attainment function approach to performance evaluation in EMO; runtime analysis of evolutionary algorithms: basic introduction; meta-model assisted (evolutionary) optimization. The papers are organized in topical sections on adaption, self-adaption and parameter tuning; differential evolution and swarm intelligence; dynamic, uncertain and constrained environments; genetic programming; multi-objective, many-objective and multi-level optimization; parallel algorithms and hardware issues; real-word applications and modeling; theory; diversity and landscape analysis.

Joint Analysis of Multiple Algorithms and Performance Measures

There has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and time complexity). Once one has developed an approach to a problem of interest, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Standard tests used for this purpose are able to consider jointly neither performance measures nor multiple competitors at once. The aim of this paper is to resolve these issues by developing statistical procedures that are able to account for multiple competing measures at the same time and to compare multiple algorithms altogether. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameters of such models, as usually the number of studied cases is very reduced in such comparisons. Data from a comparison among general purpose classifiers is used to show a practical application of our tests.

Convex Optimization Algorithms and Statistical Bounds for Learning Structured Models

Thesis (Ph.D.)--University of Washington, 2016-08

Phylogenetic inference's algorithms

Phylogenetic inference consist in the search of an evolutionary tree to explain the best way possible genealogical relationships of a set of species. Phylogenetic analysis has a large number of applications in areas such as biology, ecology, paleontology, etc. There are several criterias which has been defined in order to infer phylogenies, among which are the maximum parsimony and maximum likelihood. The first one tries to find the phylogenetic tree that minimizes the number of evolutionary steps needed to describe the evolutionary history among species, while the second tries to find the tree that has the highest probability of produce the observed data according to an evolutionary model. The search of a phylogenetic tree can be formulated as a multi-objective optimization problem, which aims to find trees which satisfy simultaneously (and as much as possible) both criteria of parsimony and likelihood. Due to the fact that these criteria are different there won't be a single optimal solution (a single tree), but a set of compromise solutions. The solutions of this set are called "Pareto Optimal". To find this solutions, evolutionary algorithms are being used with success nowadays.This algorithms are a family of techniques, which aren’t exact, inspired by the process of natural selection. They usually find great quality solutions in order to resolve convoluted optimization problems. The way this algorithms works is based on the handling of a set of trial solutions (trees in the phylogeny case) using operators, some of them exchanges information between solutions, simulating DNA crossing, and others apply aleatory modifications, simulating a mutation. The result of this algorithms is an approximation to the set of the “Pareto Optimal” which can be shown in a graph with in order that the expert in the problem (the biologist when we talk about inference) can choose the solution of the commitment which produces the higher interest. In the case of optimization multi-objective applied to phylogenetic inference, there is open source software tool, called MO-Phylogenetics, which is designed for the purpose of resolving inference problems with classic evolutionary algorithms and last generation algorithms. REFERENCES [1] C.A. Coello Coello, G.B. Lamont, D.A. van Veldhuizen. Evolutionary algorithms for solving multi-objective problems. Spring. Agosto 2007 [2] C. Zambrano-Vega, A.J. Nebro, J.F Aldana-Montes. MO-Phylogenetics: a phylogenetic inference software tool with multi-objective evolutionary metaheuristics. Methods in Ecology and Evolution. En prensa. Febrero 2016.

A natural model for architecture : the nature of the evolutionary model 1995

John Frazer's architectural work is inspired by living and generative processes. Both evolutionary and revolutionary, it explores informatin ecologies and the dynamics of the spaces between objects. Fuelled by an interest in the cybernetic work of Gordon Pask and Norbert Wiener, and the possibilities of the computer and the "new science" it has facilitated, Frazer and his team of collaborators have conducted a series of experiments that utilize genetic algorithms, cellular automata, emergent behaviour, complexity and feedback loops to create a truly dynamic architecture. Frazer studied at the Architectural Association (AA) in London from 1963 to 1969, and later became unit master of Diploma Unit 11 there. He was subsequently Director of Computer-Aided Design at the University of Ulter - a post he held while writing An Evolutionary Architecture in 1995 - and a lecturer at the University of Cambridge. In 1983 he co-founded Autographics Software Ltd, which pioneered microprocessor graphics. Frazer was awarded a person chair at the University of Ulster in 1984. In Frazer's hands, architecture becomes machine-readable, formally open-ended and responsive. His work as computer consultant to Cedric Price's Generator Project of 1976 (see P84)led to the development of a series of tools and processes; these have resulted in projects such as the Calbuild Kit (1985) and the Universal Constructor (1990). These subsequent computer-orientated architectural machines are makers of architectural form beyond the full control of the architect-programmer. Frazer makes much reference to the multi-celled relationships found in nature, and their ongoing morphosis in response to continually changing contextual criteria. He defines the elements that describe his evolutionary architectural model thus: "A genetic code script, rules for the development of the code, mapping of the code to a virtual model, the nature of the environment for the development of the model and, most importantly, the criteria for selection. In setting out these parameters for designing evolutionary architectures, Frazer goes beyond the usual notions of architectural beauty and aesthetics. Nevertheless his work is not without an aesthetic: some pieces are a frenzy of mad wire, while others have a modularity that is reminiscent of biological form. Algorithms form the basis of Frazer's designs. These algorithms determine a variety of formal results dependent on the nature of the information they are given. His work, therefore, is always dynamic, always evolving and always different. Designing with algorithms is also critical to other architects featured in this book, such as Marcos Novak (see p150). Frazer has made an unparalleled contribution to defining architectural possibilities for the twenty-first century, and remains an inspiration to architects seeking to create responsive environments. Architects were initially slow to pick up on the opportunities that the computer provides. These opportunities are both representational and spatial: computers can help architects draw buildings and, more importantly, they can help architects create varied spaces, both virtual and actual. Frazer's work was groundbreaking in this respect, and well before its time.

Evolutionary algorithms for sustainable building design

This approach to sustainable design explores the possibility of creating an architectural design process which can iteratively produce optimised and sustainable design solutions. Driven by an evolution process based on genetic algorithms, the system allows the designer to “design the building design generator” rather than to “designs the building”. The design concept is abstracted into a digital design schema, which allows transfer of the human creative vision into the rational language of a computer. The schema is then elaborated into the use of genetic algorithms to evolve innovative, performative and sustainable design solutions. The prioritisation of the project’s constraints and the subsequent design solutions synthesised during design generation are expected to resolve most of the major conflicts in the evaluation and optimisation phases. Mosques are used as the example building typology to ground the research activity. The spatial organisations of various mosque typologies are graphically represented by adjacency constraints between spaces. Each configuration is represented by a planar graph which is then translated into a non-orthogonal dual graph and fed into the genetic algorithm system with fixed constraints and expected performance criteria set to govern evolution. The resultant Hierarchical Evolutionary Algorithmic Design System is developed by linking the evaluation process with environmental assessment tools to rank the candidate designs. The proposed system generates the concept, the seed, and the schema, and has environmental performance as one of the main criteria in driving optimisation.

Biologically inspired joint control for a humanoid robot

The GuRoo is a 1.2 m tall, 23 degree of freedom humanoid constructed at the University of Queensland for research into humanoid robotics. The key challenge being addressed by the GuRoo project is the development of appropriate learning strategies for control and coordination of the robot's many joints. The development of learning strategies is seen as a way to side-step the inherent intricacy of modeling a multi-DOF biped robot. This paper outlines the approach taken to generate an appropriate control scheme for the joints of the GuRoo. The paper demonstrates the determination of local feedback control parameters using a genetic algorithm. The feedback loop is then augmented by a predictive modulator that learns a form of feed-forward control to overcome the irregular loads experienced at each joint during the gait cycle. The predictive modulator is based on the CMAC architecture. Results from tests on the GuRoo platform show that both systems provide improvements in stability and tracking of joint control.

A hybrid genetic algorithm for the optimal constrained web service selection problem in web service composition

Web service composition is an important problem in web service based systems. It is about how to build a new value-added web service using existing web services. A web service may have many implementations, all of which have the same functionality, but may have different QoS values. Thus, a significant research problem in web service composition is how to select a web service implementation for each of the web services such that the composite web service gives the best overall performance. This is so-called optimal web service selection problem. There may be mutual constraints between some web service implementations. Sometimes when an implementation is selected for one web service, a particular implementation for another web service must be selected. This is so called dependency constraint. Sometimes when an implementation for one web service is selected, a set of implementations for another web service must be excluded in the web service composition. This is so called conflict constraint. Thus, the optimal web service selection is a typical constrained ombinatorial optimization problem from the computational point of view. This paper proposes a new hybrid genetic algorithm for the optimal web service selection problem. The hybrid genetic algorithm has been implemented and evaluated. The evaluation results have shown that the hybrid genetic algorithm outperforms other two existing genetic algorithms when the number of web services and the number of constraints are large.

Partitioning composite web services for decentralized execution using a genetic algorithm

Composite web services comprise several component web services. When a composite web service is executed centrally, a single web service engine is responsible for coordinating the execution of the components, which may create a bottleneck and degrade the overall throughput of the composite service when there are a large number of service requests. Potentially this problem can be handled by decentralizing execution of the composite web service, but this raises the issue of how to partition a composite service into groups of component services such that each group can be orchestrated by its own execution engine while ensuring acceptable overall throughput of the composite service. Here we present a novel penalty-based genetic algorithm to solve the composite web service partitioning problem. Empirical results show that our new algorithm outperforms existing heuristic-based solutions.

Dynamic coast control of train movement with genetic algorithm

The railway service is now the major transportation means in most of the countries around the world. With the increasing population and expanding commercial and industrial activities, a high quality of railway service is the most desirable. Train service usually varies with the population activities throughout a day and train coordination and service regulation are then expected to meet the daily passengers' demand. Dwell time control at stations and fixed coasting point in an inter-station run are the current practices to regulate train service in most metro railway systems. However, a flexible and efficient train control and operation is not always possible. To minimize energy consumption of train operation and make certain compromises on the train schedule, coast control is an economical approach to balance run-time and energy consumption in railway operation if time is not an important issue, particularly at off-peak hours. The capability to identify the starting point for coasting according to the current traffic conditions provides the necessary flexibility for train operation. This paper presents an application of genetic algorithms (GA) to search for the appropriate coasting point(s) and investigates the possible improvement on fitness of genes. Single and multiple coasting point control with simple GA are developed to attain the solutions and their corresponding train movement is examined. Further, a hierarchical genetic algorithm (HGA) is introduced here to identify the number of coasting points required according to the traffic conditions, and Minimum-Allele-Reserve-Keeper (MARK) is adopted as a genetic operator to achieve fitter solutions.