Evolutionary Approaches to DNA Sequencing with Errors

In the paper, two evolutionary approaches to the general DNA sequencing problem, assuming both negative and positive errors in the spectrum, are compared. The older of them is based on the idea of genetic approach and is enhanced by a greedy algorithm. The newly proposed algorithm combines the tabu search and the scatter search methods. After conducting experiments with random and coding DNA sequences, our results suggest that the tabu and scatter search algorithm finds solutions of higher quality and more reliably than the genetic algorithm.     

An optimization approach to the reconstruction of positional DNA sequencing by hybridization with errors

Positional DNA sequencing by hybridization (PSBH) is a recently proposed enhancement of DNA sequencing by hybridization (SBH, potentially a powerful alternative to the DNA sequencing by gel electrophoresis). It has been discussed in many papers and applied to large scale sequencing by hybridization. However, the computational part of PSBH reconstruction is a difficult problem, especially for the occurrence of hybridization errors. So far the problem has not been solved well. Taking PSBH as a combinatorial optimization problem, a novel reconstruction approach to PSBH is presented in this paper. The proposed approach accepts both the negative and positive errors and can greatly reduce ambiguities in the reconstruction of PSBH. The computational experiment shows that our algorithm works satisfactorily and correctly on the test data, especially for the positive errors and k-tuple repetitions.     

允许长度估计误差的SBH最优重构问题及其算法

本文讨论了允许长度估计误差和杂交错误的更实际SBH(Sequencing by Hybridization)最优重构问题.通过对SBH谱集中k-tuple之间的相关信息的分析和最优重构性质的讨论,我们得到若干非最优解的删除法则和最优解的判定法则,并获得了一个能够极大地减少最优解重构随意性的动态规划计算方法.由此,我们给出了该SBH问题的一个新重构算法.该算法既允许SBH谱集含有一般杂交实验中可能出现的探针错配所产生的正错误,也允许目标DNA序列长度有估计误差,所以本文的算法具有更一般的适应性和实用性.模拟计算结果表明我们的算法也是十分有效的(即使在谱集有多达100%的正错误情况).     

A Hybrid Genetic Algorithm for the Single Machine Scheduling Problem

A hybrid genetic algorithm (HGA) is proposed for the single machine, single stage, scheduling problem in a sequence dependent setup time environment within a fixed planning horizon (SSSDP). It incorporates the elitist ranking method, genetic operators, and a hill-climbing technique in each searching area. To improve the performance and efficiency, hill climbing is performed by uniting the Wagner-Whitin Algorithm with the problem-specific knowledge. The objective of the HGA is to minimize the sum of setup cost, inventory cost, and backlog cost. The HGA is able to obtain a superior solution, if it is not optimal, in a reasonable time. The computational results of this algorithm on real life SSSDP problems are promising. In our test cases, the HGA performed up to 50% better than the Just-In-Time heuristics and 30% better than the complete batching heuristics.     

A Hybrid Genetic Algorithm for Nonconvex Function Minimization

In this paper, we consider the problem of minimizing a function in severalvariables which could be multimodal and may possess discontinuities. A newalgorithm for the problem based on the genetic technique is developed. Thealgorithm is hybrid in nature in the sense that it utilizes the genetictechnique to generate search directions, which are used in an optimizationscheme and is thus different from any other methods in the literature.The algorithm has been tested on the Rosenbrock valley functions in 2 and 4dimensions, and multimodal functions in 2 and 4 dimensions, which are of ahigh degree of difficulty. The results are compared with the Adaptive RandomSearch, and Simulated Annealing algorithms. The performance of the algorithmis also compared to recent global algorithms in terms of the number offunctional evaluations needed to obtain a global minimum and results show thatthe proposed algorithm is better than these algorithms on a set of standardtest problems. It seems that the proposed algorithm is efficient and robust.     

A Hybrid Genetic Algorithm with Boltzmann Convergence Properties

Stochastic global search algorithms such as genetic algorithms are used to attack difficult combinatorial optimization problems. However, genetic algorithms suffer from the lack of a convergence proof. This means that it is difficult to establish reliable algorithm braking criteria without extensive a priori knowledge of the solution space. The hybrid genetic algorithm presented here combines a genetic algorithm with simulated annealing in order to overcome the algorithm convergence problem. The genetic algorithm runs inside the simulated annealing algorithm and provides convergence via a Boltzmann cooling process. The hybrid algorithm was used successfully to solve a classical 30-city traveling salesman problem; it consistently outperformed both a conventional genetic algorithm and a conventional simulated annealing algorithm. This work was supported by the University of Colorado at Colorado Springs.     

混合遗传算法收敛性分析

针对遗传算法爬山能力弱但合局搜索能力强的特点 ,本文将遗传算法嵌入到基入传统优化的拟下降算法中 ,并对算法的拟下降步骤做了一定的改进 ,使得整个算法具有全局收敛性 .本文采用马尔可夫的观点进一步证明了算法的全局收敛性 ,并用极难优化的测试函数给出了数值算例 ,证明了本文算法为一种可行的全局优化算法 .     

约束装箱问题的混合遗传算法求解

本将最佳适应法和遗传算法相结合，提出了一种新的启发式混合遗传算法对具有时间约束的装箱问题进行求解，给出了具体的算法步骤，试算结果表明基于启发式算法的混合遗传算法适合于求解各种约束条件下的大规模装箱问题。     

多目标规划的一种混合遗传算法

本文利用遗传算法的全局搜索内能力及直接搜索算法的局部优化能力，提出了一种用于多目标规划的混合遗传算法．与Pareto遗传算法相比．本文提出的算法能提高多目标遗传算法优化搜索效率，并保证了能得到适舍决策者要求的Pareto最优解．最后，理论与实践证明其有有效性．     

Self-Adaptive Genetic Algorithm for Clustering

Clustering is a hard combinatorial problem which has many applications in science and practice. Genetic algorithms (GAs) have turned out to be very effective in solving the clustering problem. However, GAs have many parameters, the optimal selection of which depends on the problem instance. We introduce a new self-adaptive GA that finds the parameter setup on-line during the execution of the algorithm. In this way, the algorithm is able to find the most suitable combination of the available components. The method is robust and achieves results comparable to or better than a carefully fine-tuned non-adaptive GA.     

Extensive Testing of a Hybrid Genetic Algorithm for Solving Quadratic Assignment Problems

A robust search algorithm should ideally exhibit reasonable performance on a diverse and varied set of problems. In an earlier paper Lim et al. (Computational Optimization and Applications, vol. 15, no. 3, 2000), we outlined a class of hybrid genetic algorithms based on the k-gene exchange local search for solving the quadratic assignment problem (QAP). We follow up on our development of the algorithms by reporting in this paper the results of comprehensive testing of the hybrid genetic algorithms (GA) in solving QAP. Over a hundred instances of QAP benchmarks were tested using a standard set of parameters setting and the results are presented along with the results obtained using simple GA for comparisons. Results of our testing on all the benchmarks show that the hybrid GA can obtain good quality solutions of within 2.5% above the best-known solution for 98% of the instances of QAP benchmarks tested. The computation time is also reasonable. For all the instances tested, all except for one require computation time not exceeding one hour. The results will serve as a useful baseline for performance comparison against other algorithms using the QAP benchmarks as a basis for testing.     

改进的混合算法求解车间作业调度问题

车间作业调度问题是个典型的NP-hard问题,为了更有效的解决车间作业调度问题,提出了一种改进的混合算法(IGASA).算法设计了一种基于当前最优解的免疫算子,算子对当前最优个体中选取运行时间最少的一台机器上的工件顺序当作疫苗,并用车间调度问题的图论模型解释了此算子的合理性.最后通过大量实验证明改进的混合算法的性能的优越性,从而证明设计的免疫算子是有意义的.     

PSBH中的组合优化问题及其计算方法

本文介绍了具有部分位置信息的SBH杂交测序（Positional Sequencing by Hy-bridization,简称PSBH）实验所产生的一个重构DNA片断的组合优化问题,并讨论了该问题最优重构的计算问题.通过对PSBH提供的谱集及其位置信息的分析处理,我们获得了若干判定最优重构片断头尾的分支定界准则以及确定其非头尾位置最可能出现k-tuple的动态规划计算方法,并由此给出了该PSBH问题的一个新重构算法.该算法允许PSBH谱集含有一般杂交实验中常常可能出现探针错配所产生的正错误,并且仅仅假设PSBH的谱集、位置信息和位置长度是已知的,所以我们的算法具有更一般的适应性和实用性.此外,由于我们给出的算法能够极大地利用PSBH的谱集和位置信息所蕴含的信息确定最优重构片断头尾及其中间位置最可能出现的k-tuple,极大地减少了PSBH重构中的随意性,所以我们的算法也是有效的,模拟PSBH实验的计算结果验证了这一点.     

Sequencing by hybridization: an enhanced crossover operator for a hybrid genetic algorithm

This paper presents a genetic algorithm for an important computational biology problem. The problem appears in the computational part of a new proposal for DNA sequencing denominated sequencing by hybridization. The general usage of this method for real sequencing purposes depends mainly on the development of good algorithmic procedures for solving its computational phase. The proposed genetic algorithm is a modified version of a previously proposed hybrid genetic algorithm for the same problem. It is compared with two well suited meta-heuristic approaches reported in the literature: the hybrid genetic algorithm, which is the origin of our proposed variant, and a tabu-scatter search algorithm. Experimental results carried out on real DNA data show the advantages of using the proposed algorithm. Furthermore, statistical tests confirm the superiority of the proposed variant over the state-of-the-art heuristics.     

Project Scheduling with Multiple Modes: A Genetic Algorithm

In this paper we consider the resource-constrained project scheduling problem with multiple execution modes for each activity and makespan minimization as objective. We present a new genetic algorithm approach to solve this problem. The genetic encoding is based on a precedence feasible list of activities and a mode assignment. After defining the related crossover, mutation, and selection operators, we describe a local search extension which is employed to improve the schedules found by the basic genetic algorithm. Finally, we present the results of our thorough computational study. We determine the best among several different variants of our genetic algorithm and compare it to four other heuristics that have recently been proposed in the literature. The results that have been obtained using a standard set of instances show that the new genetic algorithm outperforms the other heuristic procedures with regard to a lower average deviation from the optimal makespan.     

A Genetic Algorithm for the Multidimensional Knapsack Problem

In this paper we present a heuristic based upon genetic algorithms for the multidimensional knapsack problem. A heuristic operator which utilises problem-specific knowledge is incorporated into the standard genetic algorithm approach. Computational results show that the genetic algorithm heuristic is capable of obtaining high-quality solutions for problems of various characteristics, whilst requiring only a modest amount of computational effort. Computational results also show that the genetic algorithm heuristic gives superior quality solutions to a number of other heuristics.     

基于DNA遗传算法的随机整数规划期望值模型求解

为了求解随机整数规划问题,提出了随机整数规划期望值模型的概念,分析了利用DNA遗传算法求解此类问题的优点,并设计了求解算法,最后通过报童问题,验证了算法的可行性和有效性.     

工程项目资源均衡优化的混合遗传算法研究

"工期固定—资源均衡"优化是指在工期一定的条件下,合理调整网络计划的某些工序,以实现资源均衡利用的一种管理方法.本文基于工程项目资源均衡优化方法中常用的遗传算法和最小矩法,提出了一种混合遗传算法.该算法首先使用遗传算法得到一个较好的初始点,然后采用最小矩法进行局部优化,克服了遗传算法局部寻优能力不足的缺陷,增强了算法的优化效果.最后通过算例分析验证了该混合算法的可行性和有效性,因而是一种较好的优化算法.     

A Hybrid Genetic Algorithm for Assembly Line Balancing

This paper presents a hybrid genetic algorithm for the simple assembly line problem, SALBP-1. The chromosome representation of the problem is based on random keys. The assignment of the operations to the workstations is based on a heuristic priority rule in which the priorities of the operations are defined by the chromosomes. A local search is used to improve the solution. The approach is tested on a set of problems taken from the literature and compared with other approaches. The computation results validate the effectiveness of the algorithm.     

关于三个非扩张映射收敛于其公共不动点的具误差的三重迭代法

在一致凸Banach空间中,研究了关于三个不同非扩张映射的具误差的三步迭代算法,并在一定条件下证明了此迭代序列收敛于其公共不动点的弱强收敛定理.