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IEEE Congress on Evolutionary Computation

作者： Brizuela, Carlos A. Luna-Taylor, Jorge E. Martinez-Perez, Israel Guillen, Hugo A. Rodriguez, David O. Beltran-Verdugo, Armando CICESE Dept Comp Sci Ensenada Baja California Mexico ITLP Dept Syst & Computat La Paz Mexico

ISBN: (纸本)9781479904549;9781479904532

The development of new technologies for the design of DNA microarrays has boosted the generation of large volumes of biological data, which requires the development of efficient computational methods for their analysis and annotation. Among these methods, biclusters generation algorithms attempt to identify coherent associations of genes and experimental conditions. In this paper, we introduce an improved version of a multi-objective genetic algorithm to find large biclusters that are, at the same time, highly homogeneous. The proposed improvement uses a group based representation for the genes-conditions associations rather than long binary strings. To assess the proposal performance the algorithm is applied to generate biclusters for two real gene expression data: Saccharomyces Cerevisiae with 2884 genes and 17 conditions, and the human B cells Lymphoma with 4026 genes and 96 conditions. The results of computational experiments show that the proposed approach outperforms current state-of-the-art algorithms on these data sets.

关键词： biclustering gene expression multi-objective genetic algorithm group based representation microarray DNA

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Portfolio management using computational intelligence approaches: Forecasting and Optimising the Stock Returns and Stock Volatilities with Fuzzy Logic, Neural Network and Evolutionary algorithms

Portfolio management using computational intelligence approa...

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作者： Prisadarng SKOLPADUNGKET University of Bradford

学位级别：博士

Portfolio optimisation has a number of constraints resulting from some practical matters and regulations. The closed-form mathematical solution of portfolio optimisation problems usually cannot include these constraints. Exhaustive search to reach the exact solution can take prohibitive amount of computational time. Portfolio optimisation models are also usually impaired by the estimation error problem caused by lack of ability to predict the future accurately. A number of multi-objective genetic algorithms are proposed to solve the problem with two objectives subject to cardinality constraints, floor constraints and round-lot constraints. Fuzzy logic is incorporated into the Vector Evaluated genetic algorithm (VEGA) to but solutions tend to cluster around a few points. Strength Pareto Evolutionary algorithm 2 (SPEA2) gives solutions which are evenly distributed portfolio along the effective front while MOGA is more time efficient. An Evolutionary Artificial Neural Network (EANN) is proposed. It automatically evolves the ANN s initial values and structures hidden nodes and layers. The EANN gives a better performance in stock return forecasts in comparison with those of Ordinary Least Square Estimation and of Back Propagation and Elman Recurrent ANNs. Adaptation algorithms for selecting a pair of forecasting models, which are based on fuzzy logic-like rules, are proposed to select best models given an economic scenario. Their predictive performances are better than those of the comparing forecasting models. MOGA and SPEA2 are modified to include a third objective to handle model risk and are evaluated and tested for their performances. The result shows that they perform better than those without the third objective.

关键词： Portfolio optimisation Realistic constraints multi-objective genetic algorithm estimation error model risk Fuzzy model selection Strength Pareto Evolutionary algorithm 2

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"Load Forecasting on the User‐side by means of Computational Intelligence algorithms"

"Load Forecasting on the User‐side by means of Computationa...

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作者： Juan Jose Cardenas Araujo Universitat Politecnica de Catalunya

学位级别：博士

Nowadays, it would be very difficult to deny the need to prioritize sustainable development through energy efficiency at all consumption levels. In this context, an energy management system (EMS) is a suitable option for continuously improving energy efficiency, particularly on the user side. An EMS is a set of technological tools that manages energy consumption information and allows its analysis. EMS, in combination with information technologies, has given rise to intelligent EMS (iEMS), which, aside from lending support to monitoring and reporting functions as an EMS does, it has the ability to model, forecast, control and diagnose energy consumption in a predictive way. The main objective of an iEMS is to continuously improve energy efficiency (on-line) as automatically as possible. The core of an iEMS is its load modeling forecasting system (LMFS). It takes advantage of historical information on energy consumption and energy-related variables in order to model and forecast load profiles and, if available, generator profiles. These models and forecasts are the main information used for iEMS applications for control and diagnosis. That is why in this thesis we have focused on the study, analysis and development of LMFS on the user side. The fact that the LMFS is applied on the user side to support an iEMS means that specific characteristics are required that in other areas of load forecasting they are not. First of all, the user-side load profiles (LPs) have a higher random behavior than others, as for example, in power system distribution or generation. This makes the modeling and forecasting process more difficult. Second, on the user side --for example an industrial user-- there is a high number and variety of places that can be monitored, modeled and forecasted, as well as their precedence or nature. Thus, on the one hand, an LMFS requires a high degree of autonomy to automatically or autonomously generate the demanded models. And on the other hand, it needs

关键词： Energy efficiency Load profile Load forecasting User side Energy management system Intelligent energy management system Load modeling and forecasting system Adaptability Autonomy Computational intelligence ANFIS ANN genetic algorithm multi-objective genetic algorithm Hilbert-Huang transform Hilbert-spectrum analysis

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Antenna Optimization in Long-Term Evolution Networks

Antenna Optimization in Long-Term Evolution Networks

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作者： Deng, Qichen KTH Royal Institute of Technology

学位级别：master

The aim of this master thesis is to study algorithms for automatically tuning antenna parameters to improve the performance of the radio access part of a telecommunication network and user experience. There are four dierent optimization algorithms, Stepwise Minimization algorithm, Random Search algorithm, Modied Steepest Descent algorithm and multi-objective genetic algorithm to be applied to a model of a radio access network. The performances of all algorithms will be evaluated in this thesis. Moreover, a graphical user interface which is developed to facilitate the antenna tuning simulations will also be presented in the appendix of the report

关键词： multi-objective Optimization Non Dominated (Pareto Optimal) Solutions Pareto Front Cost Function Stepwise Minimization algorithm Random Search algorithm Modied Steepest Descent algorithm multi-objective genetic algorithm Graphical User Interface (GUI). Natural Sciences Mathematics Naturvetenskap Matematik Physics Chemistry Mathematics fysik/kemi/matematik Master of Science - Mathematics Teknologie masterexamen - Matematik Mathematics Matematik

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multi-Stage Hybrid Rocket Conceptual Design for Micro-Satellites Launch using genetic algorithm

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TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 2012年第4期55卷 229-236页

作者： Kitagawa, Yosuke Kitagawa, Koki Nakamiya, Masaki Kanazaki, Masahiro Shimada, Toru Tokyo Metropolitan Univ Dept Aeronaut & Astronaut Hino Tokyo Japan Japan Aerosp Explorat Agcy ISAS JAXA Sagamihara Kanagawa Japan

The multi-objective genetic algorithm (MOGA) is applied to the multi-disciplinary conceptual design problem for a three-stage launch vehicle (LV) with a hybrid rocket engine (HRE). MOGA is an optimization tool used for multi-objective problems. The parallel coordinate plot (PCP), which is a data mining method, is employed in the post-process in MOGA for design knowledge discovery. A rocket that can deliver observing micro-satellites to the sun-synchronous orbit (SSO) is designed. It consists of an oxidizer tank containing liquid oxidizer, a combustion chamber containing solid fuel, a pressurizing tank and a nozzle. The objective functions considered in this study are to minimize the total mass of the rocket and to maximize the ratio of the payload mass to the total mass. To calculate the thrust and the engine size, the regression rate is estimated based on an empirical model for a paraffin (FT-0070) propellant. Several non-dominated solutions are obtained using MOGA, and design knowledge is discovered for the present hybrid rocket design problem using a PCP analysis. As a result, substantial knowledge on the design of an LV with an HRE is obtained for use in space transportation.

关键词： Hybrid Rocket multidisciplinary Design Optimization multi-objective genetic algorithm

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multi-objective genetic algorithms, NSGA-II and SPEA2, for Document Clustering

Multi-Objective Genetic Algorithms, NSGA-II and SPEA2, for D...

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International Conference on Advanced Software Engineering and Its Applications (ASEA 2011)

作者： Lee, Jung Song Choi, Lim Cheon Park, Soon Cheol Jeonbuk Natl Univ Div Elect & Informat Engn Jeonju Jeonbuk South Korea

ISBN: (纸本)9783642272066

This paper proposes the multi-objective genetic algorithm (MOGA) for document clustering. The studied, hierarchical agglomerative algorithms. k-means algorithm and general genetic algorithm (GA) are more progressing in document clustering. However, in hierarchical agglomerative algorithms, efficiency is a problem (0(n(2)logn)), k-means algorithm depends on too much the initial centroids, and general GA can converge to the local optimal value when defining an objective function which is not suitable. In this paper, two of MOGA's algorithms, NSGA-II and SPEA2 are applied to document clustering in order to complete these disadvantages. We compare to NSGA-II. SPEA2 and the existing clustering algorithms (k-means. general GA). Our experimental results show the average values of NSGA-II and SPEA2 are about 28% higher the clustering performance than the k-means algorithm and about 17% higher the clustering performance than the general GA.

关键词： Document Clustering genetic algorithm multi-objective genetic algorithm NSGA-II SPEA2

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A non-dominated sorting genetic algorithm for a bi-objective pick-up and delivery problem

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ENGINEERING OPTIMIZATION 2012年第3期44卷 305-325页

作者： Velasco, N. Dejax, P. Gueret, C. Prins, C. Univ Los Andes Dept Ingn Ind Bogota Colombia Ecole Mines Nantes UMR 6597 IRCCyN F-44307 Nantes 3 France Univ Technol Troyes FRE 2848 ICD F-10010 Troyes France

Some companies must transport their personnel within facilities. This is especially the case for oil companies that use helicopters to transport engineers, technicians and assistant personnel from platform to platform. This operation has the potential to become expensive if the transportation routes are not correctly planned and provide a bad quality of service. Here this issue is modelled as a pick-up and delivery problem where a set of transportation requests should be scheduled in routes, minimizing the total transportation cost while the most urgent requests are satisfied by priority. To solve the problem, a method based on a Non-dominated Sorting genetic algorithm (NSGA-II) is proposed. This algorithm is tested on both randomly generated and real instances provided by a petroleum company. The results show that the proposed algorithm improves the best-known solutions.

关键词： routing pick-up and delivery personnel transportation non-dominated sorting algorithm multi-objective genetic algorithm

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An Efficient genetic algorithm for Subgraph Isomorphism 12

An Efficient Genetic Algorithm for Subgraph Isomorphism

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14th International Conference on genetic and Evolutionary Computation Conference (GECCO)

作者： Choi, Jaeun Yoon, Yourim Moon, Byung-Ro Seoul Natl Univ Sch Comp Sci & Engn Seoul 151744 South Korea

ISBN: (纸本)9781450311779

In this paper we propose a multi-objective genetic algorithm for the subgraph isomorphism problem. Usually, the number of different edges between two graphs has been used as a fitness function. This approach has limitations in that it only considers directly-visible characteristics of current solutions, not considering the potential for being an optimal solution. We designed a fitness function in which solutions with higher potential can be rated high. This new fitness function has good properties such as transforming the solution space globally convex and improving the performance of local heuristics and genetic algorithms. Experimental results show that the suggested approach brings a considerable improvement in performance and efficiency.

关键词： genetic algorithm subgraph isomorphism fitness function multi-objective genetic algorithm

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A Parasitic Layer-Based Reconfigurable Antenna Design by multi-objective Optimization

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2012年第6期60卷 2690-2701页

作者： Yuan, Xiaoyan Li, Zhouyuan Rodrigo, Daniel Mopidevi, Hema Swaroop Kaynar, Oguz Jofre, Lluis Cetiner, Bedri A. Utah State Univ Dept Elect & Comp Engn Logan UT 84322 USA Tech Univ Catalonia Dept Signal Theory & Commun Barcelona 08034 Spain

A parasitic layer-based multifunctional reconfigurable antenna (MRA) design based on multi-objective genetic algorithm optimization used in conjunction with full-wave EM analysis is presented. The MRA is capable of steering its beam into three different directions (theta(i) = -30 degrees, 0 degrees, 30 degrees) simultaneously with polarization reconfigurability (P-j = Linear, Circular) having six different modes of operation. The MRA consists of a driven microstrip-fed patch element and a reconfigurable parasitic layer, and is designed to be compatible with IEEE-802.11 WLAN standards (5-6 GHz range). The parasitic layer is placed on top of the driven patch. The upper surface of the parasitic layer has a grid of 5 x 5 electrically small rectangular-shaped metallic pixels, i.e., reconfigurable parasitic pixel surface. The EM energy from the driven patch element couples to the reconfigurable parasitic pixel surface by mutual coupling. The adjacent pixels are connected/disconnected by means of switching, thereby changing the geometry of pixel surface, which in turn changes the current distribution over the parasitic layer, results in the desired mode of operation in beam direction and polarization. A prototype of the designed MRA has been fabricated on quartz substrate. The results from simulations and measurements agree well indicating 8 dB gain in all modes of operation.

关键词： Beam steering full-wave analysis multi-objective genetic algorithm reconfigurable antenna

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Optimization of novel heat exchanger design for the application to low temperature lift heat pump

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ENERGY 2012年第1期42卷 204-212页

作者： Lee, Hoseong Saleh, Khaled Hwang, Yunho Radermacher, Reinhard Univ Maryland Dept Mech Engn Ctr Environm Energy Engn College Pk MD 20742 USA

The low temperature lift heat pump (LTLHP) utilizes a small difference between the condensing and evaporating temperatures of a working fluid. It requires a larger heat transfer area, a higher volume flow rate, and a higher temperature of heat source fluid, as compared to the typical heat pump system. In order to improve the performance of conventional plate heat exchangers, a novel heat exchanger with new geometries has been developed for application in the LTLHP. The main design development strategies were regulating the flow area ratio and offsetting plates in order to balance the heat transfer and pressure drop of the heat exchanger. The design parameters of the novel heat exchanger design were optimized with multi-scale approaches. Once the refrigerant-side geometry is properly designed according to the water-side performance, the overall heat transfer capacity of the novel heat exchanger is predicted to be higher than that of PHX by 53-204%. This can decrease the cost of the heat exchanger and increase the performance of the LTLHP system. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Low temperature lift heat pump multi-objective optimization Kriging meta-model Approximation-assisted optimization multi-objective genetic algorithm

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