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Search Results: 1 - 10 of 12956 matches for " Genetic Algorithms "
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Optimization of UMTS Network Planning Using Genetic Algorithms  [PDF]
Fabio Garzia, Cristina Perna, Roberto Cusani
Communications and Network (CN) , 2010, DOI: 10.4236/cn.2010.23028
Abstract: The continuously growing of cellular networks complexity, which followed the introduction of UMTS technology, has reduced the usefulness of traditional design tools, making them quite unworthy. The purpose of this paper is to illustrate a design tool for UMTS optimized net planning based on genetic algorithms. In particular, some utilities for 3G net designers, useful to respect important aspects (such as the environmental one) of the cellular network, are shown.
Genetic Algorithms-based Optimization of Cable Stayed Bridges  [PDF]
Venkat Lute, Akhil Upadhyay, Krishna Kumar Singh
Journal of Software Engineering and Applications (JSEA) , 2011, DOI: 10.4236/jsea.2011.410066
Abstract: Optimum design of cable stayed bridges depends on number of parameters. Design of Cable stayed bridge satisfying all practical constraints is challenging to the designers. Considering the huge number of design variables and practical constraints, Genetic Algorithms (GA) is most suitable for optimizing the cable stayed bridge. In the present work the optimum design is carried out by taking total material cost of bridge as objective function. During problem formulation most of the practical design variables and constraints are considered. Using genetic algorithms some parametric studies such as effect of geometric nonlinearity, effect of grouping of cables, effect of practical site constraints on tower height and side span, effect of bridge material, effect of cable layout, effect of extra-dosed bridges on optimum relative cost have been presented. Data base is prepared for new designers to estimate the relative cost of bridge.
A New Approach to the Optimization of the CVRP through Genetic Algorithms  [PDF]
Mariano Frutos, Fernando Tohmé
American Journal of Operations Research (AJOR) , 2012, DOI: 10.4236/ajor.2012.24058
Abstract: This paper presents a new approach to the analysis of complex distribution problems under capacity constraints. These problems are known in the literature as CVRPs (Capacitated Vehicle Routing Problems). The procedure introduced in this paper optimizes a transformed variant of a CVRP. It starts generating feasible clusters and codifies their ordering. In the next stage the procedure feeds this information into a genetic algorithm for its optimization. This makes the algorithm independent of the constraints and improves its performance. Van Breedam problems have been used to test this technique. While the results obtained are similar to those in other works, the processing times are longer.
Evolutionary Techniques for Reverse Auctions  [PDF]
Shubhashis Kumar Shil, Samira Sadaoui, Malek Mouhoub
Intelligent Control and Automation (ICA) , 2013, DOI: 10.4236/ica.2013.44044

Winner determination is one of the main challenges in combinatorial auctions. However, not much work has been done to solve this problem in the case of reverse auctions using evolutionary techniques. This has motivated us to propose an improvement of a genetic algorithm based method, we have previously proposed, to address two important issues in the context of combinatorial reverse auctions: determining the winner(s) in a reasonable processing time, and reducing the procurement cost. In order to evaluate the performance of our proposed method in practice, we conduct several experiments on combinatorial reverse auctions instances. The results we report in this paper clearly demonstrate the efficiency of our new method in terms of processing time and procurement cost.

Economic and Feasibility Analysis for Stand–alone Solar Photovoltaic Generation System  [PDF]
Jeeng-Min Ling, Ping-Hsun Liu
Energy and Power Engineering (EPE) , 2013, DOI: 10.4236/epe.2013.54B069
Abstract: The paper presents a feasibility computing approach to solve the optimal planning problem applied to Stand-alone Photovoltaic (SPV) system by considering the reliability requirement and economical performance. Evaluation technique based on genetic algorithm to get global optimum capacity of solar array and battery in a SPV system is more efficiently. Explicit strategy selects proper values of systems' parameters improving local exploration and avoiding trapped in local optimum. Different requirements of system reliability are investigated to achieve the optimal planning of a SPV system. Sensitivity analysis of components' cost and load profiles are conducted to demonstrate the impacts of system uncertainty. The solar radiation and temperature data from the Central Weather Bureau of Taiwan at four different locations were used.
Optimum Design for CLD Laminate Plates Using Genetic Algorithms  [PDF]
Guang-Min Luo, Tsung-Yen Hsieh
Open Journal of Composite Materials (OJCM) , 2014, DOI: 10.4236/ojcm.2014.42012

The optimizations of constrained layered damped (CLD) laminated structures are discussed in this study. Genetic algorithms (GAs) are employed as the search tool for optimization because these algorithms are suitable for solving optimization problems involving multiple discrete variable combinations. The numerical computation packages, ANSYS and MATLAB, have been used to estimate the optimum stacking sequence of CLD laminated structures. MATLAB package is used to achieve GAs process, and ANSYS package is used to proceed the structural analysis. This study successfully developed a numerical simulation mechanism for optimizing CLD adhesion efficiency by implementing GAs and the finite element method. The loss coefficients of the CLD damping layer vary with vibration frequency and failure constraints of CLD laminated plates are considered in objective function. In addition, the modified plasticity analysis (MPA) is used to increase the search efficiency of GAs and simply plastic analysis.

Selecting Oil Wells for Hydraulic Fracturing: A Comparison between Genetic-Fuzzy and Neuro Fuzzy Systems  [PDF]
Virgílio José Martins Ferreira Filho, Ant?nio Orestes de Salvo Castro
American Journal of Operations Research (AJOR) , 2014, DOI: 10.4236/ajor.2014.44020

Hydraulic fracturing is widely used to increase oil well production and to reduce formation damage. Reservoir studies and engineering analyses are carried out to select the wells for this kind of operation. As the reservoir parameters have some diffuse characteristics, Fuzzy Inference Systems (FIS) have been tested for these selection processes in the last few years. This paper compares the performance of a neuro fuzzy system and a genetic fuzzy system used for selecting wells for hydraulic fracturing, with knowledge acquired from an operational data base to set the SIF membership functions. The training data and the validation data used were the same for both systems. We concluded that, despite the genetic fuzzy system being a newer process, it obtained better results than the neuro fuzzy system. Another conclusion was that, as the genetic fuzzy system can work with constraints, the membership functions setting kept the consistency of variable linguistic values.

Local Search-Inspired Rough Sets for Improving Multiobjective Evolutionary Algorithm  [PDF]
Ahmed A. EL-Sawy, Mohamed A. Hussein, El-Sayed Mohamed Zaki, Abd Allah A. Mousa
Applied Mathematics (AM) , 2014, DOI: 10.4236/am.2014.513192

In this paper we present a new optimization algorithm, and the proposed algorithm operates in two phases. In the first one, multiobjective version of genetic algorithm is used as search engine in order to generate approximate true Pareto front. This algorithm is based on concept of co-evolution and repair algorithm for handling nonlinear constraints. Also it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept e-dominance. Then, in the second stage, rough set theory is adopted as local search engine in order to improve the spread of the solutions found so far. The results, provided by the proposed algorithm for benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable for solving real-world application problems.

Evolutionary Approach to Forex Expert Advisor Generation  [PDF]
Alaa Eldin M. Ibrahim
Intelligent Information Management (IIM) , 2014, DOI: 10.4236/iim.2014.63014

We have developed a genetic algorithm approach for automatically generating expert advisors, computer programs that trade automatically in the financial markets. Our system, known as GenFx or Genetic Forex, evaluates evolutionarily generated expert advisors strategies using predetermined fitness functions to automatically prioritize parents for breeding. GenFx simulates several key factors in natural selection. It employs a multiple generation breeding population, a notion of gender, and the concept of aging to maintain diversity while providing many breeding opportunities to highly successful offspring. The approach is also especially efficient running in a multiple processor, multiple selection-strategy mode using multiple settings. We found out that a multi-processor gender-based running of the system outperformed all single runs of the system. This system is inspired by GenShade, a previous system that we have developed for evolutionary generating procedural textures. The methods described in this paper are not limited to the Forex market or financial problems only but are applicable to many other fields.

Um algoritmo evolutivo híbrido para a forma??o de células de manufatura em sistemas de produ??o
Trindade, áthila Rocha;Ochi, Luiz Satoru;
Pesquisa Operacional , 2006, DOI: 10.1590/S0101-74382006000200005
Abstract: the manufacturing cell formation problem (mcfp) is a crucial component of a cell production design in a manufacturing system. this problem is composed by a set of parts of products to be manufactured and machines. the objective is to construct manufacturing clusters by associating products with cell machines. this paper presents a new hybrid evolutionary algorithm to solve the mcfp. computational results with the proposed algorithm on a set of instances available in the literature are also presented. for 8 out of 36 instances considered, the propose method overcame the previous results from the literature and for 26 instances, the same best solutions were found.
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