Kathleen Steinhöfel

Search Based Approaches to Component Selection and Prioritization for the Next Release Problem

This paper addresses the problem of determining the next set of releases in the course of software evolution. It formulates both ranking and selection of candidate software components as a series of feature subset selection problems to which search based software engineering can be applied. The approach is automated using greedy and simulated annealing algorithms and evaluated using a set of software components from the component base of a large telecommunications organization. The results are compared to those obtained by a panel of (human) experts. The results show that the two automated approaches convincingly outperform the expert judgment approach

Two simulated annealing-based heuristics for the job shop scheduling problem

Abstract In this paper, we present two simulated annealing-based algorithms for the classical, general job shop scheduling problem where the objective is to minimize the makespan. We consider sets of jobs consisting of tasks and sets of machines, which can handle at most one task at a time. To represent the problem, we employ the model of disjunctive graphs. Simulated annealing has been applied to this problem earlier, e.g., by Van Laarhoven et al., where the neighborhood function is based on reversing a single arc of a longest path of the underlying graph. In our approach, we analyze a neighborhood function which involves a non-uniform generation probability. To obtain the neighbors of a schedule, we reverse more than a single arc of longest paths and perform a control on the increase of the makespan. The selection of the arcs depends on the number of longest paths to which a particular arc belongs. Furthermore, we have designed two cooling schedules which employ a detailed analysis of the objective function. Depending on the specified neighborhood relation, the expected run-times can be either O(n2+e) or O(n3+e/m) for the first cooling schedule and O(n5/2+e·m1/2) or O(n7/2+e/m1/2) for the second cooling schedule, where n is the number of tasks, m the number of machines and e represents O ( ln ln n/ ln n) . Our computational experiments on small to large benchmark problems have shown that within short series of consecutive trials relatively stable results equal or close to optimal solutions are repeatedly obtained, including the well-known benchmark problems FT10 and LA38. We could improve five upper bounds for the YN1, YN4, SWV12, SWV13, and SWV15 benchmark problems, e.g., for SWV13 the gap between the lower and the former upper bound has been shortened by about 57%. In our approach we rely only on basic information about the given problem instance.

A Local Move Set for Protein Folding in Triangular Lattice Models

The HP model is one of the most popular discretized models for the protein folding problem, i.e., for computationally predicting the three-dimensional structure of a protein from its amino acid sequence. This model considers the interactions between hydrophobic amino acids to be the driving force in the folding process. Thus, it distinguishes between polar and hydrophobic amino acids only and asks for an embedding of the amino acid sequence into a rectangular grid lattice which maximizes the number of neighboring pairs (contacts) of hydrophobic amino acids in the lattice. In this paper, we consider an HP-like model which uses a more appropriate grid structure, namely the 2D triangular grid and the face-centered cubic lattice in 3D. We consider a local-search approach for finding an optimal embedding. For defining the local-search neighborhood, we design a move set, the so-called pull moves, and prove its reversibility and completeness. We then use these moves for a tabu search algorithm which is experimentally shown to lead into optimum energy configurations and improve the current best results for several sequences in 2D and 3D.

A hybrid approach to protein folding problem integrating constraint programming with local search

BackgroundThe protein folding problem remains one of the most challenging open problems in computational biology. Simplified models in terms of lattice structure and energy function have been proposed to ease the computational hardness of this optimization problem. Heuristic search algorithms and constraint programming are two common techniques to approach this problem. The present study introduces a novel hybrid approach to simulate the protein folding problem using constraint programming technique integrated within local search.ResultsUsing the face-centered-cubic lattice model and 20 amino acid pairwise interactions energy function for the protein folding problem, a constraint programming technique has been applied to generate the neighbourhood conformations that are to be used in generic local search procedure. Experiments have been conducted for a few small and medium sized proteins. Results have been compared with both pure constraint programming approach and local search using well-established local move set. Substantial improvements have been observed in terms of final energy values within acceptable runtime using the hybrid approach.ConclusionConstraint programming approaches usually provide optimal results but become slow as the problem size grows. Local search approaches are usually faster but do not guarantee optimal solutions and tend to stuck in local minima. The encouraging results obtained on the small proteins show that these two approaches can be combined efficiently to obtain better quality solutions within acceptable time. It also encourages future researchers on adopting hybrid techniques to solve other hard optimization problems.

Protein Folding Simulation by Two-Stage Optimization

This paper proposes a two-stage optimization approach for protein folding simulation in the FCC lattice, inspired from the phenomenon of hydrophobic collapse. Given a protein sequence, the first stage of the approach produces compact protein structures with the maximal number of contacts among hydrophobic monomers, using the CPSP tools for optimal structure prediction in the HP model. The second stage uses those compact structures as starting points to further optimize the protein structure for the input sequence by employing simulated annealing local search and a 20 amino acid pairwise interactions energy function. Experiment results with PDB sequences show that compact structures produced by the CPSP tools are up to two orders of magnitude better, in terms of the pairwise energy function, than randomly generated ones. Also, initializing simulated annealing with these compact structures, yields better structures in fewer iterations than initializing with random structures. Hence, the proposed two-stage optimization outperforms a local search procedure based on simulated annealing alone.

Fast parallel heuristics for the job shop scheduling problem

The paper is dealing with parallelized versions of simulated annealing-based heuristics for the classical job shop scheduling problem. The scheduling problem is represented by the disjunctive graph model and the objective is to minimize the length of longest paths. The problem is formulated for l jobs where each job has to process exactly one task on each of the m machines. The calculation of longest paths is the critical computation step of our heuristics and we utilize a parallel algorithm for this particular problem where we take into account the specific properties of job shop scheduling. In our heuristics, we employ a neighborhood relation which was introduced by Van Laarhoven et al. (Operations Research 40(1) (1992) 113-25). To obtain a neighbor, a single arc from a longest path is reversed and these transition steps always guarantee the feasibility of schedules. We designed two cooling schedules for homogeneous Markov chains and additionally we investigated a logarithmic cooling schedule for inhomogeneous Markov chains. Given O(n3) processors and a known npper bound Λ = Λ(l, m) for the length of longest paths, the expected run-times of parallelized versions are O(n log n log Λ) for the first cooling schedule and O(n2(log3/2 n)m1/2 log Λ) for the second cooling schedule, where n = lm is the number of tasks. For the logarithmic cooling schedule, a speed-up of O(n/(log n log Λ)) can be achieved. When Markov chains of constant length are assumed, we obtain a polylogarithmic run-time of O(log n log Λ) for the first cooling schedule. The analysis of famous benchmark problems led us to the conjecture that Λ ≤ O(l + m) could be a uniform upper bound for the completion time of job shop scheduling problems with l jobs on m machines. Although the number of processors is very large, the particular processors are extremely simple and the parallel processing system is suitable for hardware implementations.

Stochastic protein folding simulation in the three-dimensional HP-model

We present results from three-dimensional protein folding simulations in the HP-model on ten benchmark problems. The simulations are executed by a simulated annealing-based algorithm with a time-dependent cooling schedule. The neighbourhood relation is determined by the pull-move set. The results provide experimental evidence that the maximum depth D of local minima of the underlying energy landscape can be upper bounded by D<n(2/3). The local search procedure employs the stopping criterion (m/delta)(D/gamma), where m is an estimation of the average number of neighbouring conformations, gamma relates to the mean of non-zero differences of the objective function for neighbouring conformations, and 1-delta is the confidence that a minimum conformation has been found. The bound complies with the results obtained for the ten benchmark problems.

Genetic local search for multicast routing with pre-processing by logarithmic simulated annealing

Over the past few years, several local search algorithms have been proposed for various problems related to multicast routing in the off-line mode. We describe a population-based search algorithm for cost minimisation of multicast routing. The algorithm utilises the partially mixed crossover operation (PMX) under the elitist model: for each element of the current population, the local search is based upon the results of a landscape analysis that is executed only once in a pre-processing step; the best solution found so far is always part of the population. The aim of the landscape analysis is to estimate the depth of the deepest local minima in the landscape generated by the routing tasks and the objective function. The analysis employs simulated annealing with a logarithmic cooling schedule (logarithmic simulated annealing-LSA). The local search then performs alternating sequences of descending and ascending steps for each individual of the population, where the length of a sequence with uniform direction is controlled by the estimated value of the maximum depth of local minima. We present results from computational experiments on three different routing tasks, and we provide experimental evidence that our genetic local search procedure that combines LSA and PMX performs better than algorithms using either LSA or PMX only.

Search--based approaches to the component selection and prioritization problem

This poster paper addresses the problem of choosing sets of software components to combine in component--based software engineering. It formulates both ranking and selection problems as feature subset selection problems to which search based software engineering can be applied. We will consider selection and ranking of elements from a set of software components from the component base of a large telecommunications organisation.

Server Placements, Roman Domination and other Dominating Set Variants

Dominating sets in their many variations model a wealth of optimization problems like facility location or distributed file sharing. For instance, when a request can occur at any node in a graph and requires a server at that node, a minimum dominating set represents a minimum set of servers that serve an arbitrary single request by moving a server along at most one edge. This paper studies domination problems for two requests. For the problem of placing a minimum number of servers such that two requests at different nodes can be served with two different servers (called win-win), we present a logarithmic approximation, and we prove that nothing better is possible. We show that the same is true for Roman domination, the well studied problem variant that asks for each vertex to either possess its own server or to have a neighbor with two servers. Still the same is true if each idle server can move along one edge while the first of both requests is being served. For planar graphs, we propose a PTAS for Roman domination (and show that nothing better exists), and we get a constant approximation for win-win.