Design of a second life product family from the perspective of the remanufacturing agent

This thesis presents a method of solving a newly posed Second Life Product Family Design problem. This is unique in that the architecture of the product is not speci ed to be identical to one of the recaptured products, rather it is determined through optimization. The problem is framed using Conjoint Analysis and the Multi Nomial Logit Model, formatted with respect to components available for inclusion in the nal products and then solved using an implementation of Genetic Algorithms. The solution method is also encapsulated in a software module which can be disseminated to industrial users without a background in optimization or familiarity with Genetic Algorithms. A case study is performed to determine the e ectiveness of the proposed solution method, and analyze the in uences di erent market conditions and component similarities can have on the optimal design. It is concluded that the proposed method converges to an optimal Second Life Product Family Design

Experimentally Attainable Optimal Pulse Shapes Obtained with the Aid of Genetic Algorithms

We propose a methodology to design optimal pulses for achieving quantum optimal control on molecular systems. Our approach constrains pulse shapes to linear combinations of a fixed number of experimentally relevant pulse functions. Quantum optimal control is obtained by maximizing a multi-target fitness function with genetic algorithms. As a first application of the methodology we generated an optimal pulse that successfully maximized the yield on a selected dissociation channel of a diatomic molecule. Our pulse is obtained as a linear combination of linearly chirped pulse functions. Data recorded along the evolution of the genetic algorithm contained important information regarding the interplay between radiative and diabatic processes. We performed a principal component analysis on these data to retrieve the most relevant processes along the optimal path. Our proposed methodology could be useful for performing quantum optimal control on more complex systems by employing a wider variety of pulse shape functions.Comment: 7 pages, 6 figure

A first attempt at constructing genetic programming expressions for EEG classification

Proceeding of: 15th International Conference on Artificial Neural Networks ICANN 2005, Poland, 11-15 September, 2005In BCI (Brain Computer Interface) research, the classification of EEG signals is a domain where raw data has to undergo some preprocessing, so that the right attributes for classification are obtained. Several transformational techniques have been used for this purpose: Principal Component Analysis, the Adaptive Autoregressive Model, FFT or Wavelet Transforms, etc. However, it would be useful to automatically build significant attributes appropriate for each particular problem. In this paper, we use Genetic Programming to evolve projections that translate EEG data into a new vectorial space (coordinates of this space being the new attributes), where projected data can be more easily classified. Although our method is applied here in a straightforward way to check for feasibility, it has achieved reasonable classification results that are comparable to those obtained by other state of the art algorithms. In the future, we expect that by choosing carefully primitive functions, Genetic Programming will be able to give original results that cannot be matched by other machine learning classification algorithms.Publicad

A Hybrid Design Optimization Method using Enriched Craig-Bampton Approach

A hybrid design optimization method is presented which combines a number of techniques such as Component Mode Synthesis (CMS), Design of Computer Experiments and Neural Networks for surrogate modeling with Genetic Algorithms and Sequential Quadratic Programming for optimization. In the method, the FE analysis is decomposed and reduced by a well-known CMS technique called the Craig-Bampton method. Since the optimization method requires CMS calculations of the updated model at each of its iterations due to the changes in the design variables, one can either reuse the reduction basis of the initial components or compute new reduction basis for the condensation of the system matrices. The first option usually leads to inaccurate results and the last one increases the omputation time. In the method, instead of using one of these options, the Enriched Craig-Bampton method, proposed by Masson et al., is employed for efficient optimization. New basis for the modified components are generated by extending the corresponding initial reduction basis with a set of static residual vectors which are calculated using prior knowledge of the initial component designs. Thus, time consuming complete component analyzes are prevented. A theoretical test problem is used for the demonstration of the method

An optimization method for dynamics of structures with repetitive component patterns

The occurrence of dynamic problems during the operation of machinery may have devastating effects on a product. Therefore, design optimization of these products becomes essential in order to meet safety criteria. In this research, a hybrid design optimization method is proposed where attention is focused on structures having repeating patterns in their geometries. In the proposed method, the analysis is decomposed but the optimization problem itself is treated as a whole. The model of an entire structure is obtained without modeling all the repetitive components using the merits of the Component Mode Synthesis method. Backpropagation Neural Networks are used for surrogate modeling. The optimization is performed using two techniques: Genetic Algorithms (GAs) and Sequential Quadratic Programming (SQP). GAs are utilized to increase the chance of finding the location of the global optimum and since this optimum may not be exact, SQP is employed afterwards to improve the solution. A theoretical test problem is used to demonstrate the method

Development of genetic algorithm based classification and cluster analysis methods for analytical data

Thesis (Doctoral)--İzmir Institute of Technology, Chemistry, İzmir, 2009Includes bibliographical references (leaves: 151-158)Text in English; Abstract: Turkish and Englishxviii, 158 leavesIn this study genetic algorithm based classification and clustering methods were aimed to develop for the spectral data. The developed methods were completely achieved hybridization of nature inspired algorithm (genetic algorithms, GAs) to other classification or clustering methods. The first method was genetic algorithm based principal component analysis (GAPCAD), and the second was genetic algorithm based discriminant analysis (GADA). Both methods were performed to achieve the best discrimination between the olive oil and vegetable oil samples. The classifications of samples were examined directly from their spectral data obtained from using near infrared spectrometry, Fourier transform infrared (FTIR) spectrometry, and spectrofluorometry. The GA was used to optimize the performance of classification or clustering techniques. on training set in order to maximize the correct classification of acceptable and unacceptable samples or samples of dissimilar properties and to reduce the spectral data by wavelength selection. After GA optimization the classification results of training set were controlled by validation set. Lastly, the success of both algorithms was compared to the results of PCA and SIMCA

Quantum Genetic Algorithm with Individuals in Multiple Registers

Genetic algorithms are heuristic optimization techniques inspired by Darwinian evolution, which are characterized by successfully finding robust solutions for optimization problems. Here, we propose a subroutine-based quantum genetic algorithm with individuals codified in independent registers. This distinctive codification allows our proposal to depict all the fundamental elements characterizing genetic algorithms, i.e. population-based search with selection of many individuals, crossover, and mutation. Our subroutine-based construction permits us to consider several variants of the algorithm. For instance, we firstly analyze the performance of two different quantum cloning machines, a key component of the crossover subroutine. Indeed, we study two paradigmatic examples, namely, the biomimetic cloning of quantum observables and the Bu\v zek-Hillery universal quantum cloning machine, observing a faster average convergence of the former, but better final populations of the latter. Additionally, we analyzed the effect of introducing a mutation subroutine, concluding a minor impact on the average performance. Furthermore, we introduce a quantum channel analysis to prove the exponential convergence of our algorithm and even predict its convergence-ratio. This tool could be extended to formally prove results on the convergence of general non-unitary iteration-based algorithms