Fixed-Parameter Evolutionary Algorithms and the Vertex Cover Problem

In this paper, we consider multi-objective evolutionary algorithms for the Vertex Cover problem in the context of parameterized complexity. We consider two different measures for the problem. The first measure is a very natural multi-objective one for the use of evolutionary algorithms and takes into account the number of chosen vertices and the number of edges that remain uncovered. The second fitness function is based on a linear programming formulation and proves to give better results. We point out that both approaches lead to a kernelization for the Vertex Cover problem. Based on this, we show that evolutionary algorithms solve the vertex cover problem efficiently if the size of a minimum vertex cover is not too large, i.e., the expected runtime is bounded by O(f(OPT)?n ^c ), where c is a constant and f a function that only depends on OPT. This shows that evolutionary algorithms are randomized fixed-parameter tractable algorithms for the vertex cover problem.     

Fixed-Parameter Complexity of Minimum Profile Problems

The profile of a graph is an integer-valued parameter defined via vertex orderings; it is known that the profile of a graph equals the smallest number of edges of an interval supergraph. Since computing the profile of a graph is an NP-hard problem, we consider parameterized versions of the problem. Namely, we study the problem of deciding whether the profile of a connected graph of order n is at most n−1+k, considering k as the parameter; this is a parameterization above guaranteed value, since n−1 is a tight lower bound for the profile. We present two fixed-parameter algorithms for this problem. The first algorithm is based on a forbidden subgraph characterization of interval graphs. The second algorithm is based on two simple kernelization rules which allow us to produce a kernel with linear number of vertices and edges. For showing the correctness of the second algorithm we need to establish structural properties of graphs with small profile which are of independent interest. A preliminary version of the paper is published in Proc. IWPEC 2006, LNCS vol. 4169, 60–71.     

Fixed-Parameter Algorithms for CLOSEST STRING and Related Problems

CLOSEST STRING is one of the core problems in the field of consensus word analysis with particular importance for computational biology. Given k strings of the same length and a nonnegative integer d , find a ``center string'' s such that none of the given strings has the Hamming distance greater than d from s . CLOSEST STRING is NP-complete. In biological applications, however, d is usually very small. We show how to solve CLOSEST STRING in linear time for fixed d —the exponential growth in d is bounded by O(dd) . We extend this result to the closely related problems d -MISMATCH and DISTINGUISHING STRING SELECTION. Moreover, we also show that CLOSEST STRING is solvable in linear time when k is fixed and d is arbitrary. In summary, this means that CLOSEST STRING is fixed-parameter tractable with respect to parameter d and with respect to parameter k . Finally, the practical usefulness of our findings is substantiated by some experimental results.     

Fixed-Parameter Algorithms for CLOSEST STRING and Related Problems

CLOSEST STRING is one of the core problems in the field of consensus word analysis with particular importance for computational biology. Given k strings of the same length and a nonnegative integer d , find a ``center string' s such that none of the given strings has the Hamming distance greater than d from s . CLOSEST STRING is NP-complete. In biological applications, however, d is usually very small. We show how to solve CLOSEST STRING in linear time for fixed d —the exponential growth in d is bounded by O(d ^d ) . We extend this result to the closely related problems d -MISMATCH and DISTINGUISHING STRING SELECTION. Moreover, we also show that CLOSEST STRING is solvable in linear time when k is fixed and d is arbitrary. In summary, this means that CLOSEST STRING is fixed-parameter tractable with respect to parameter d and with respect to parameter k . Finally, the practical usefulness of our findings is substantiated by some experimental results.     

欧氏Steiner最小树问题的智能优化算法

欧氏平面内连接固定原点的最小树长问题，即欧氏Steiner最小树问题，为组合优化中的NP难题，因此合理的方法是寻找启发式算法。该文给出了两种智能优化算法——模拟退火法和蚂蚁算法。首先概述智能优化算法并将中面划分成网格，然后分别介绍两种算法的原理及实现过程，最后通过一系列计算实验，测试了算法的运行性能，获得了较好的效果。     

一种求解最小割集问题的新思路

从本质上来说,最小割集问题与最大流问题是同一个问题。由于后者的实用性更强,人们对它投入的关注与研究也更多,因而实际中是通过最大流问题来求最小割集问题。最大流-最小割集定理给出了一种用最大流算法求最小割集问题的方法,但在实际应用中,这种方法有时显得繁冗并有些迂回。文章首先介绍了最大流、最小割集的相关概念,然后从实际应用出发提出了一种用最大流求流图最小割集的新算法。随后证明了该算法的正确性,并举例说明了这种算法思想在其它方面的应用。     

求图着色问题的新算法

图着色问题是NP-难度的问题。基于两种传统的启发式算法，提出了两种新的求解策略，由此给出了求图着色问题的两个新算法。与传统算法相比，其中一个新算法在时间复杂度不变的条件下，解的质量有明显提高；另一个则在时间复杂度稍有增加的前提下，进一步较显著地提高了所得解的质量。     

On the Fixed-Parameter Tractability of the Maximum Connectivity Improvement Problem

Theory of Computing Systems - In the Maximum Connectivity Improvement (MCI) problem, we are given a directed graph G = (V,E) and an integer B and we are asked to find B new edges to be added to G...     

New Approximation Algorithms for Minimum Cycle Bases of Graphs

We consider the problem of computing an approximate minimum cycle basis of an undirected non-negative edge-weighted graph G with m edges and n vertices; the extension to directed graphs is also discussed. In this problem, a {0,1} incidence vector is associated with each cycle and the vector space over mathbbF₂mathbb{F}_{2} generated by these vectors is the cycle space of G. A set of cycles is called a cycle basis of G if it forms a basis for its cycle space. A cycle basis where the sum of the weights of the cycles is minimum is called a minimum cycle basis of G.     

On the Minimum Feedback Vertex Set Problem: Exact and Enumeration Algorithms

We present a time algorithm finding a minimum feedback vertex set in an undirected graph on n vertices. We also prove that a graph on n vertices can contain at most 1.8638 ⁿ minimal feedback vertex sets and that there exist graphs having 105 ^n/10≈1.5926 ⁿ minimal feedback vertex sets. Preliminary extended abstracts of this paper appeared in the proceedings of SWAT’06 [29] and IWPEC’06 [18]. Additional support of F.V. Fomin, S. Gaspers and A.V. Pyatkin by the Research Council of Norway. The work of A.V. Pyatkin was partially supported by grants of the Russian Foundation for Basic Research (project code 05-01-00395), INTAS (project code 04–77–7173). I. Razgon is supported by Science Foundation Ireland (Grant Number 05/IN/I886).     

Approximation Algorithms for Minimum K -Cut

Let G=(V,E) be a complete undirected graph, with node set V={v ₁ , . . ., v _n } and edge set E . The edges (v _i ,v _j ) ∈ E have nonnegative weights that satisfy the triangle inequality. Given a set of integers K = { k _i } _i=1 ^p , the minimum K-cut problem is to compute disjoint subsets with sizes { k _i } _i=1 ^p , minimizing the total weight of edges whose two ends are in different subsets. We demonstrate that for any fixed p it is possible to obtain in polynomial time an approximation of at most three times the optimal value. We also prove bounds on the ratio between the weights of maximum and minimum cuts. Received September 4, 1997; revised July 15, 1998.     

最小费用最大流问题的一种新算法

现有的最小费用最大流算法都有自身的缺陷,增广链的选取不当会给计算带来不便,同时费用也达不到理想的效果。鉴于对最小费用最大流算法的增广链选取和最小费用的探索,文章通过对费用差的定义给出了一种求最小费用最大流的新算法。新算法的原则是优先选择费用差最小的有向路径进行增广,当费用差相同时就选择修正后的路径。通过对最小费用最大流算法的改进,新算法易理解且便于计算。通过实例说明了新算法的有效性和执行效率。