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Enhancing Multi-model Inference with Natural SelectionChing-Wei Cheng (7582487) 30 October 2019 (has links)
<div>Multi-model inference covers a wide range of modern statistical applications such as variable selection, model confidence set, model averaging and variable importance.</div><div>The performance of multi-model inference depends on the availability of candidate models, whose quality has been rarely studied in literature. In this dissertation, we study genetic algorithm (GA) in order to obtain high-quality candidate models. Inspired by the process of natural selection, GA performs genetic operations such as selection, crossover and mutation iteratively to update a collection of potential solutions (models) until convergence. The convergence properties are studied based on the Markov chain theory and used to design an adaptive termination criterion that vastly reduces the computational cost. In addition, a new schema theory is established to characterize how the current model set is improved through evolutionary process. Extensive numerical experiments are carried out to verify our theory and demonstrate the empirical power of GA, and new findings are obtained for two real data examples. </div>
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Computational Studies of Microscopic Superfluidity in the 4He ClustersWairegi, Angeline R. 01 May 2016 (has links)
The physics that result in the decoupling of a molecule from a bosonic solvent at 0 K are studied. Fixed-node diffusion Monte Carlo (FNDMC) coupled with a Genetic Algorithm is used to perform simulations of the bosonic droplets doped with various molecules. The efficacy and accuracy of this approach is tested on a strongly coupled 2-dimensional quartic oscillator with excellent results. This algorithm is then applied to 4He-CO and 4He-HCN clusters respectively in an effort to determine the factors that result in the onset of microscopic superfluidity. The decoupling of the doped molecule from the bosonic solvent is found to be, primarily, a result of the combined effect of the repulsive interaction between the helium atoms and bose symmetry. The effects of rotor size versus molecular anisotropy in a NH3 molecule seeded into a 4He droplet is studied as well. Simulations are done using the accurate rotational constants (B0=9.945 cm-1, C0=6.229 cm-1) and using "fudged" versions of the rotational constants (Bfudged=0.9945 cm-1, Cfudged=0.6229 cm-1) for the |0011〉state. The simulations done with the fudged rotational constants experience a slightly smaller reduction than those done using the accurate rotational constants. This is attributed to the importance of molecular anisotropy versus the size of larger rotational constants in molecules whose rotational constants fall in an intermediate regime.
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Multi-Stop Routing Optimization: A Genetic Algorithm ApproachHommadi, Abbas 01 May 2018 (has links)
In this research, we investigate and propose new operators to improve Genetic Algorithm’s performance to solve the multi-stop routing problem. In a multi-stop route, a user starts at point x, visits all destinations exactly once, and then return to the same starting point. In this thesis, we are interested in two types of this problem. The first type is when the distance among destinations is fixed. In this case, it is called static traveling salesman problem. The second type is when the cost among destinations is affected by traffic congestion. Thus, the time among destinations changes during the day. In this case, it is called time-dependent traveling salesman problem. This research proposes new improvements on genetic algorithm to solve each of these two optimization problems.
First, the Travelling Salesman Problem (TSP) is one of the most important and attractive combinatorial optimization problems. There are many meta-heuristic algorithms that can solve this problem. In this paper, we use a Genetic Algorithm (GA) to solve it. GA uses different operators: selection, crossover, and mutation. Sequential Constructive Crossover (SCX) and Bidirectional Circular Constructive Crossover (BCSCX) are efficient to solve TSP. Here, we propose a modification to these crossovers. The experimental results show that our proposed adjustment is superior to SCX and BCSCX as well as to other conventional crossovers (e.g. Order Crossover (OX), Cycle Crossover (CX), and Partially Mapped Crossover (PMX)) in term of solution quality and convergence speed. Furthermore, the GA solver, that is improved by applying inexpensive local search operators, can produce solutions that have much better quality within reasonable computational time.
Second, the Time-Dependent Traveling Salesman Problem (TDTSP) is an interesting problem and has an impact on real-life applications such as a delivery system. In this problem, time among destinations fluctuates during the day due to traffic, weather, accidents, or other events. Thus, it is important to recommend a tour that can save driver’s time and resources. In this research, we propose a Multi-Population Genetic Algorithm (MGA) where each population has different crossovers. We compare the proposed MG against Single-Population Genetic Algorithm (SGA) in terms of tour time solution quality. Our finding is that MGA outperforms SGA. Our method is tested against real-world traffic data [1] where there are 200 different instances with different numbers of destinations. For all tested instances, MGA is superior on average by at least 10% (for instances with size less than 50) and 20% (for instances of size 50) better tour time solution compared to SGA with OX and SGA with PMX operators, and at least 4% better tour time compared toga with SCX operator.
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[en] EVOLUTIONARY SYNTHESIS IN NANOTECHNOLOGY / [pt] SÍNTESE EVOLUCIONÁRIA EM NANOTECNOLOGIALEONE PEREIRA MASIERO 22 August 2006 (has links)
[pt] A Nanotecnologia teve seus primeiros conceitos
introduzidos pelo físico
americano Richard Feynman em 1959, em sua famosa palestra
intitulada
There´s plenty of room at the bottom (Ainda há muito
espaço sobrando no
fundo). Já a Inteligência Computacional tem sido utilizada
com sucesso em
diversas áreas no meio acadêmico e industrial. Este
trabalho investiga o
potencial dos Algoritmos Genéticos na otimização e síntese
de dispositivos e
estruturas na área de Nanotecnologia, através de 3 tipos
de aplicações distintas:
síntese de circuitos eletrônicos moleculares, projeto de
novos polímeros
condutores e otimização de parâmetros de OLEDs (Organic
Light-Emitting
Diodes). A síntese de circuitos eletrônicos moleculares é
desenvolvida com base
em Hardware Evolucionário (EHW - Evolvable Hardware) e tem
como principais
elementos dois dispositivos moleculares simulados em
SPICE: o diodo molecular
e o transistor molecular. O projeto de novos polímeros
condutores é baseado em
uma metodologia que combina uma aproximação tight-binding
(hamiltoniano de
Hückel simplificado) que representa a estrutura eletrônica
de uma cadeia
polimérica, empregando um AG com avaliação distribuída
como mecanismo de
síntese. Finalmente, a otimização de parâmetros de OLEDs é
desenvolvida por
meio de um método que modela o comportamento elétrico do
dispositivo com
multicamadas, onde cada camada possui uma proporção de MTE
(material
transportador de elétrons) e uma proporção de MTB
(material transportador de
buracos). As aplicações apresentam resultados que
comprovam que o apoio de
técnicas de Inteligência Computacional como os Algoritmos
Genéticos no mundo
nanométrico pode trazer benefícios para a criação e o
desenvolvimento de novas
tecnologias. / [en] The first Nanotechnology concepts were introduced by the
American
physicist Richard Feynman in 1959, in his famous lecture
entitled There´s plenty
of room at the bottom. Computational Intelligence has been
successfully used in
various areas in the academic and industrial worlds. This
work investigates the
potential of Genetic Algorithms in the optimization and
synthesis of devices and
structures in the Nanotechnology domain, by means of 3
types of distinct
applications: synthesis of molecular electronic circuits,
design of new conducting
polymers and optimization of OLEDs (Organic Light-Emitting
Diodes) parameters.
The synthesis of molecular electronic circuits is
developed based on the
Evolvable Hardware (EHW) paradigm and has as main elements
two molecular
devices simulated in SPICE: the molecular diode and the
molecular transistor.
The design of new conducting polymers is based on a
methodology that
combines an approximated tight-binding (simplified Huckel
Hamiltonian) that
represents the electronic structure of a polymer chain,
using a GA with distributed
evaluation as the synthesis mechanism. Finally, the
optimization of OLEDs
parameters is developed by means of a method that models
the electric behavior
of multi-layer devices, where each layer has a ratio of
electron transport material
(ETM) to hole transport material (HTM). The applications
present results that
demonstrate that the use of Computational Intelligence
techniques, as Genetic
Algorithms, in the nanometer world can bring benefits for
the creation and
development of new technologies.
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A generic platform for the evolution of hardwareBedi, Abhishek January 2009 (has links)
Evolvable Hardware is a technique derived from evolutionary computation applied to a hardware design. The term evolutionary computation involves similar steps as involved in the human evolution. It has been given names in accordance with the electronic technology like, Genetic Algorithm (GA), Evolutionary Strategy (ES) and Genetic Programming (GP). In evolutionary computing, a configured bit is considered as a human chromosome for a genetic algorithm, which has to be downloaded into hardware. Early evolvable hardware experiments were conducted in simulation and the only elite chromosome was downloaded to the hardware, which was labelled as Extrinsic Hardware. With the invent of Field Programmable Gate Arrays (FPGAs) and Reconfigurable Processing Units (RPUs), it is now possible for the implementation solutions to be fast enough to evaluate a real hardware circuit within an evolutionary computation framework; this is called an Intrinsic Evolvable Hardware. This research has been taken in continuation with project 'Evolvable Hardware' done at Manukau Institute of Technology (MIT). The project was able to manually evolve two simple electronic circuits of NAND and NOR gates in simulation. In relation to the project done at MIT this research focuses on the following: To automate the simulation by using In Circuit Debugging Emulators (IDEs), and to develop a strategy of configuring hardware like an FPGA without the use of their company supplied in circuit debugging emulators, so that the evolution of an intrinsic evolvable hardware could be controlled, and is hardware independent. As mentioned, the research conducted here was able to develop an evolvable hardware friendly Generic Structure which could be used for the development of evolvable hardware. The structure developed was hardware independent and was able to run on various FPGA hardware’s for the purpose of intrinsic evolution. The structure developed used few configuration bits as compared to current evolvable hardware designs.
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A Constraint Handling Strategy for Bit-Array Representation GA in Structural Topology OptimizationWang, Shengyin, Tai, Kang 01 1900 (has links)
In this study, an improved bit-array representation method for structural topology optimization using the Genetic Algorithm (GA) is proposed. The issue of representation degeneracy is fully addressed and the importance of structural connectivity in a design is further emphasized. To evaluate the constrained objective function, Deb's constraint handling approach is further developed to ensure that feasible individuals are always better than infeasible ones in the population to improve the efficiency of the GA. A hierarchical violation penalty method is proposed to drive the GA search towards the topologies with higher structural performance, less unusable material and fewer separate objects in the design domain in a hierarchical manner. Numerical results of structural topology optimization problems of minimum weight and minimum compliance designs show the success of this novel bit-array representation method and suggest that the GA performance can be significantly improved by handling the design connectivity properly. / Singapore-MIT Alliance (SMA)
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A flexible control system for flexible manufacturing systemsScott, Wesley Dane 30 September 2004 (has links)
A flexible workcell controller has been developed using a three level control hierarchy (workcell, workstation, equipment). The cell controller is automatically generated from a model input by the user. The model consists of three sets of graphs. One set of graphs describes the process plans of the parts produced by the manufacturing system, one set describes movements into, out of and within workstations, and the third set describes movements of parts/transporters between workstations. The controller uses an event driven Petri net to maintain state information and to communicate with lower level controllers. The control logic is contained in an artificial neural network. The Petri net state information is used as the input to the neural net and messages that are Petri net events are output from the neural net. A genetic algorithm was used to search over alternative operation choices to find a "good" solution. The system was fully implemented and several test cases are described.
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Genetic Algorithm Applied to Generalized Cell Formation Problems / Les algorthmes génétiques appliqués aux problèmes de formation de cellules de production avec routages et processes alternatifsVin, Emmanuelle 19 March 2010 (has links)
The objective of the cellular manufacturing is to simplify the management of the
manufacturing industries. In regrouping the production of different parts into clusters,
the management of the manufacturing is reduced to manage different small
entities. One of the most important problems in the cellular manufacturing is the
design of these entities called cells. These cells represent a cluster of machines that
can be dedicated to the production of one or several parts. The ideal design of a
cellular manufacturing is to make these cells totally independent from one another,
i.e. that each part is dedicated to only one cell (i.e. if it can be achieved completely
inside this cell). The reality is a little more complex. Once the cells are created,
there exists still some traffic between them. This traffic corresponds to a transfer of
a part between two machines belonging to different cells. The final objective is to
reduce this traffic between the cells (called inter-cellular traffic).
Different methods exist to produce these cells and dedicated them to parts. To
create independent cells, the choice can be done between different ways to produce
each part. Two interdependent problems must be solved:
• the allocation of each operation on a machine: each part is defined by one or
several sequences of operations and each of them can be achieved by a set of
machines. A final sequence of machines must be chosen to produce each part.
• the grouping of each machine in cells producing traffic inside and outside the
cells.
In function of the solution to the first problem, different clusters will be created to
minimise the inter-cellular traffic.
In this thesis, an original method based on the grouping genetic algorithm (Gga)
is proposed to solve simultaneously these two interdependent problems. The efficiency
of the method is highlighted compared to the methods based on two integrated algorithms
or heuristics. Indeed, to form these cells of machines with the allocation
of operations on the machines, the used methods permitting to solve large scale
problems are generally composed by two nested algorithms. The main one calls the
secondary one to complete the first part of the solution. The application domain goes
beyond the manufacturing industry and can for example be applied to the design of
the electronic systems as explained in the future research.
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Design Of A Skid-steer LoaderYalcin, Tugce 01 September 2012 (has links) (PDF)
Skid-steer loaders are also called mini loaders. Skid-steer loaders are capable of zero
turning radiuses, which make them extremely maneuverable and suitable for
confined spaces. The aim of this thesis study is to design the loader mechanism for
skid-steer loaders. Primarily, the loader mechanism synthesis will be performed to
determine the basic link dimensions for the mechanism of the loader. Genetic
algorithm will be used in the design process. Besides, the hydraulic cylinders
dimensions and working pressure of the loader mechanism will be chosen according
to the forces that will be applied. After the link dimensions of the loader are
determined, 3D modeling of the loader mechanism will be performed. Afterwards,
the finite element analysis of the system will be carried out. Finally, improvements will
be made on the model according to the results of the analysis.
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Wireless Heterogeneous Transmitter Placement Based on the Variable-Length Genetic AlgorithmChang, Hui-Chun 28 August 2007 (has links)
Wireless network placement of transmitters, such as base stations for 2G
and 3G, access points for WLAN, is a NP-hard problem, since many factors
have to be considered, like QoS, coverage, cost, etc. In wireless network
placement problem, the goal is to find a set of transmitters which achieves the
widest coverage on a given map and spends the minimal cost. In this thesis,
we propose a novel variable-length genetic algorithm for solving this problem.
Most of existing methods for solving wireless network placement problem, to
our best knowledge, users must assign an upper bound or a total number of
transmitters for placement. Unlike these existing methods, the proposed
algorithm can search the optimal number of transmitters automatically. In
addition, the proposed algorithm can find near optimal solutions even in
heterogeneous transmitters placement problem, i.e., transmitters with different
power radius or cost. The results on several benchmarks are very close to the
optimal solutions, which validate the capability of the proposed method in
finding the numbers, the types, are the positions of transmitters in
heterogeneous wireless network environment.
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