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A New Genetic Algorithm Applied to Multi-Objectives Optimal of Upgrading Infrastructure in NGWN  [PDF]
Dac-Nhuong Le, Nhu Gia Nguyen, Dac Binh Ha, Vinh Trong Le
Communications and Network (CN) , 2013, DOI: 10.4236/cn.2013.53B2042
Abstract: A problem of upgrading to the Next Generation Wireless Network (NGWN) is backward compatibility with pre-existing networks, the cost and operational benefit of gradually enhancing networks, by replacing, upgrading and installing new wireless network infrastructure elements that can accommodate both voice and data demand. In this paper, we propose a new genetic algorithm has double population to solve Multi-Objectives Optimal of Upgrading Infrastructure (MOOUI) problem in NGWN. We modeling network topology for MOOUI problem has two levels in which mobile users are sources and both base stations and base station controllers are concentrators. Our objective function is the sources to concentrators connectivity cost as well as the cost of the installation, connection, replacement, and capacity upgrade of infrastructure equipment. We generate two populations satisfy constraints and combine them to build solutions and evaluate the performance of my algorithm with data randomly generated. Numerical results show that our algorithm is a promising approach to solve this problem.
A Hybrid Genetic Algorithm for the Multiple Crossdocks Problem
Zhaowei Miao,Ke Fu,Feng Yang
Mathematical Problems in Engineering , 2012, DOI: 10.1155/2012/316908
Abstract: We study a multiple crossdocks problem with supplier and customer time windows, where any violation of time windows will incur a penalty cost and the flows through the crossdock are constrained by fixed transportation schedules and crossdock capacities. We prove this problem to be -hard in the strong sense and therefore focus on developing efficient heuristics. Based on the problem structure, we propose a hybrid genetic algorithm (HGA) integrating greedy technique and variable neighborhood search method to solve the problem. Extensive experiments under different scenarios were conducted, and results show that HGA outperforms CPLEX solver, providing solutions in realistic timescales.
Vertical decomposition with Genetic Algorithm for Multiple Sequence Alignment
Farhana Naznin, Ruhul Sarker, Daryl Essam
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-353
Abstract: In this paper, we have proposed a Vertical Decomposition with Genetic Algorithm (VDGA) for Multiple Sequence Alignment (MSA). In VDGA, we divide the sequences vertically into two or more subsequences, and then solve them individually using a guide tree approach. Finally, we combine all the subsequences to generate a new multiple sequence alignment. This technique is applied on the solutions of the initial generation and of each child generation within VDGA. We have used two mechanisms to generate an initial population in this research: the first mechanism is to generate guide trees with randomly selected sequences and the second is shuffling the sequences inside such trees. Two different genetic operators have been implemented with VDGA. To test the performance of our algorithm, we have compared it with existing well-known methods, namely PRRP, CLUSTALX, DIALIGN, HMMT, SB_PIMA, ML_PIMA, MULTALIGN, and PILEUP8, and also other methods, based on Genetic Algorithms (GA), such as SAGA, MSA-GA and RBT-GA, by solving a number of benchmark datasets from BAliBase 2.0.The experimental results showed that the VDGA with three vertical divisions was the most successful variant for most of the test cases in comparison to other divisions considered with VDGA. The experimental results also confirmed that VDGA outperformed the other methods considered in this research.Multiple Sequence Alignment (MSA), the simultaneous alignment among three or more nucleotide or amino acid sequences, is one of the most essential tools in molecular biology. The goal of multiple sequence alignment is to align sequences according to their evolutionary relationships. For small lengths and small numbers of sequences, it is possible to create the alignment manually. However, efficient algorithms are essential for good alignments with more than eight sequences [1]. The existing algorithms can be classified into three main categories, exact, progressive and iterative. The iterative approaches can be of two
Optimizing a multi-objectives flow shop scheduling problem by a novel genetic algorithm
N. Shahsavari Pour,R. Tavakkoli-Moghaddam,H. Asadi
International Journal of Industrial Engineering Computations , 2013, DOI: 10.5267/j.ijiec.2013.03.008
Abstract: Flow-shop problems, as a typical manufacturing challenge, have become an interesting area of research. The primary concern is that the solution space is huge and, therefore, the set of feasible solutions cannot be enumerated one by one. In this paper, we present an efficient solution strategy based on a genetic algorithm (GA) to minimize the makespan, total waiting time and total tardiness in a flow shop consisting of n jobs and m machines. The primary objective is to minimize the job waiting time before performing the related operations. This is a major concern for some industries such as food and chemical for planning and production scheduling. In these industries, there is a probability of the decay and deterioration of the products prior to accomplishment of operations in workstation, due to the increase in the waiting time. We develop a model for a flowshop scheduling problem, which uses the planner-specified weights for handling a multi-objective optimization problem. These weights represent the priority of planning objectives given by managers. The results of the proposed GA and classic GA are analyzed by the analysis of variance (ANOVA) method and the results are discussed.
International Journal of Engineering Science and Technology , 2010,
Abstract: Multiple sequence alignment (MSA) is one of the multi-dimensional problems in biology. This paper describes a new approach to solve MSA, a NP-hard problem using modified Genetic Algorithm with new mutation operator. A web based tool iMAGA (Intron Multiple Alignment using Genetic Algorithm) is developed for aligning the intron sequences in order to find the pattern. It has two modules (i) iExtractor/ iClassifier which extracts and classifies introns (ii) iAligner/ Pattern Finder which aligns the intron sequences and finds the patterns. iAligner, the core module of the tool aligns any type of sequences (DNA, RNA, Protein & Intron). In this module GA is applied in which the chromosome consists of gap positions. On applying conventional mutation operator leads to problems like repetition and increase in number of gap positions. To overcome theseproblems, a newly designed mutation operator X-Shuffler is proposed.To validate the alignment, the sum-of-pairs score is used to compare iMAGA with widely used tools. The data sets chosen from the standard BaliBASE, SMART and OXBENCH benchmark alignment suite. To validate the pattern obtained using iMAGA tool, the similarity percentage of the pattern is compared with MEME, a widely used motif finder. Dataset of Saccharomyces Cerviceae with 254 intron sequences are used to prove this work. The tool is available at www.imaga.bicpu.edu.in.
A Novel Genetic Algorithm using Helper Objectives for the 0-1 Knapsack Problem  [PDF]
Jun He,Feidun He,Hongbin Dong
Computer Science , 2014,
Abstract: The 0-1 knapsack problem is a well-known combinatorial optimisation problem. Approximation algorithms have been designed for solving it and they return provably good solutions within polynomial time. On the other hand, genetic algorithms are well suited for solving the knapsack problem and they find reasonably good solutions quickly. A naturally arising question is whether genetic algorithms are able to find solutions as good as approximation algorithms do. This paper presents a novel multi-objective optimisation genetic algorithm for solving the 0-1 knapsack problem. Experiment results show that the new algorithm outperforms its rivals, the greedy algorithm, mixed strategy genetic algorithm, and greedy algorithm + mixed strategy genetic algorithm.
Fuzzy preference of multiple decision-makers in solving multiobjective optimisation problems using genetic algorithm
Surafel Luleseged Tilahun
Maejo International Journal of Science and Technology , 2012,
Abstract: Most real-life optimisation problems involve multiple objective functions.Finding a solution that satisfies the decision-maker is very difficult owing to conflict between the objectives. Furthermore, the solution depends on the decision-maker’s preference. Metaheuristic solution methods have become common tools to solve these problems. The task of obtaining solutions that take account of a decision-maker’s preference is at the forefront of current research. It is also possible to have multipledecision-makers with different preferences and with different decision-making powers. It may not be easy to express a preference using crisp numbers. In this study, the preferences of multiple decision-makers were simulated and a solution based on a genetic algorithm was developed to solve multi-objective optimisation problems. The preferences werecollected as fuzzy conditional trade-offs and they were updated while running the algorithm interactively with the decision-makers. The proposed method was tested using well-known benchmark problems. The solutions were found to converge around the Pareto front of the problems.
Ant Colony with Genetic Algorithm Based on Planar Graph for Multiple Sequence Alignment  [PDF]
Xuyu Xiang,Dafan Zhang,Jiaohua Qin,Yuanyuan Fu
Information Technology Journal , 2010,
Abstract: Multiple Sequence Alignment (MSA), known as NP-complete problem, is among the most important and challenging tasks in computational biology. For multiple sequence alignment, it is difficult to solve this type of problems directly and always results in exponential complexity. In order to effectively solve the MSA problem, in this study, we present a novel algorithm of ant colony with genetic algorithm (ACG) based on the planar graph representation for MSA. Firstly, the planar graph is described a representation for multiple sequences that took every possible aligning result into account by defining the representation of gap insertion, the value of heuristic information in every optional path and scoring rule for the processes of MSA. Secondly, we use an ant colony with genetic algorithm to find the better path that denotes a better aligning result for multidimensional graph. Experimental results show that ACG could bring about a rise in the quality of MSA when compared with standard Clustal algorithm.
Core Hunter: an algorithm for sampling genetic resources based on multiple genetic measures
Chris Thachuk, José Crossa, Jorge Franco, Susanne Dreisigacker, Marilyn Warburton, Guy F Davenport
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-243
Abstract: We present Core Hunter, an advanced stochastic local search algorithm for selecting core subsets. Core Hunter is able to find core subsets having more genetic diversity and better average genetic distance than the current state-of-the-art algorithms for all genetic distance and diversity measures we evaluated. Furthermore, Core Hunter can attempt to optimize any number of genetic measures simultaneously, based on the preference of the user. Notably, Core Hunter is able to select significantly smaller core subsets, which retain all unique alleles from a reference collection, than state-of-the-art algorithms.Core Hunter is a highly effective and flexible tool for sampling genetic resources and establishing core subsets. Our implementation, documentation, and source code for Core Hunter is available at http://corehunter.org webciteGenetic resources stored in gene banks are usually sampled with the purpose of evaluating and utilizing them efficiently, as well as studying phenotypic and genotypic diversity, identifying duplicate accessions, and forming core subsets. The aim of the latter activity is to preserve in the sample as much of the diversity present in the original collection as possible. Core subset selection can be based on varying criteria including phenotypic traits or various forms of molecular marker data including, but not limited to, single nucleotide polymorphisms (SNP), amplified fragment length polymorphisms (AFLP), random amplified polymorphic DNA (RAPD), and simple sequence repeats (SSR). A simple example considering SNP data is given in Figure 1. The concept of core collections (or core subsets) was introduced to increase the efficiency of characterizing and utilizing the collections stored in gene banks while preserving the genetic diversity of the collection [1,2].In many instances, gene bank curators and genetic resource conservation managers need to stratify their sampling procedure prior to forming a core subset. The criteria for stratifying sa
Genetic Algorithm Based Approach for Obtaining Alignment of Multiple Sequences  [PDF]
Ruchi Gupta,Dr. Pankaj Agarwa,Dr. A. K. Soni
International Journal of Advanced Computer Sciences and Applications , 2013,
Abstract: This paper presents genetic algorithm based solution for determing alignment of multiple molecular sequences. Two datasets from DNA families Canis_familiaris and galaxy dataset have been considered for experimental work & analysis. Genetic operators like cross over rate, mutation rate can be defined by the user. Experiments & observations were recorded w.r.t variable parameters like fixed population size vs variable number of generations & vice versa, variable crossover & mutation rates. Comparative evaluation in terms of measure of fitness accuracy is also carried out w.r.t existing MSA tools like Maft, Kalign. Experimental results show that the proposed solution does offer better fitness accuracy rates.
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