1464370
Integer programming approaches to haplotype inference by pure parsimony
In 2003, Gusfield introduced the haplotype inference by pure parsimony (HIPP) problem and presented an integer program (IP) that quickly solved many simulated instances of the problem. Although it solved well on small instances, Gusfield's IP can be of exponential size in the worst case. Several authors have presented polynomial-sized IPs for the problem. In this paper, we further the work on IP approaches to HIPP. We extend the existing polynomial-sized IPs by introducing several classes of valid cuts for the IP. We also present a new polynomial-sized IP formulation that is a hybrid between two existing IP formulations and inherits many of the strengths of both. Many problems that are too complex for the exponential-sized formulations can still be solved in our new formulation in a reasonable amount of time. We provide a detailed empirical comparison of these IP formulations on both simulated and real genotype sequences. Our formulation can also be extended in a variety of ways to allow errors in the input or model the structure of the population under consideration
14054763
The use of functional domains to improve transmembrane protein topology prediction
Transmembrane proteins affect vital cellular functions and pathogenesis, and are a focus of drug design. It is difficult to obtain diffraction quality crystals to study transmembrane protein structure. Computational tools for transmembrane protein topology prediction fill in the gap between the abundance of transmembrane proteins and the scarcity of known membrane protein structures. Their prediction accuracy is still inadequate: TMHMM, the current state-of-the-art method, has less than 52% accuracy in topology prediction on one set of transmembrane proteins of known topology. Based on the observation that there are functional domains that occur preferentially internal or external to the membrane, we have extended the model of TMHMM to incorporate functional domains, using a probabilistic approach originally developed for computational gene finding. Our extension is better than TMHMM in predicting the topology of transmembrane proteins. As prediction of functional domain improves, our system's prediction accuracy will likely improve as well.
2947153
Dimensions of machine learning in design
Many of the design systems developed in recent years incorporate some machine learning. The number of such systems already available, and the multitude of design learning opportunities that are slowly being revealed, suggest that the time is ripe to attempt to put these developments into a systematic framework. Consequently, in this paper we present a set of dimensions for machine learning in design research. We hope that it can be used as a guide for comparing existing work, and that it may suggest new directions for future exploration in this area.
2693848
A parallel heuristic for quadratic assignment problems
Abstract The quadratic assignment problem represents an important class of problems with applications as diverse as facility layout and data analysis. The importance of these applications coupled with the fact that the quadratic assignment problem is NP-hard has encouraged the development of heuristics because optimal seeking procedures have been restricted to very small versions of the problem. This paper describes a new parallel heuristic, SAGA, for the quadratic assignment problem. SAGA is a cascaded hybrid of a genetic algorithm and simulated annealing. In addition to details regarding SAGA and its implementation, this paper also describes the performance of SAGA on two standard problems taken from the literature. The results from these problems show SAGA to be superior to the most commonly employed heuristic in solution quality, and for large problems it is also superior in solution time.
317687
A Cost Model for Determining the Optimal Number of Software Test Cases
A probabilistic model is presented that demonstrates the optimal number of software test cases required in situations where the following can be estimated as independent parameters: (1) the cost per test; (2) the cost per error if undetected until field implementation; (3) the number of software executions over its lifetime; (4) the number of possible different executions; and (5) the number of faults embedded in the software. A formula is derived by the use of calculus and is solved by approximation techniques. Tables of the optimal number of tests over a range of parameter values are presented to illustrate the results. The model serves as a basis for further research efforts to improve the accuracy of input variable estimation. >
1332842
A functional framework for the implementation of genetic algorithms: Comparing Haskell and Standard ML
We present our experience of developing a generic functiona l framework for the implementation of genetic algorithms (GAs). We have implemented two versions of the framework, one in Haskell and the other in Standard ML. This paper describes the structure of the framework, and com pares the performance of the two versions using the Royal Road Problem in g enetic algorithms. The Haskell version is found to be vastly slower than the equi valent Standard ML version, and the reasons for this have not been exposed by p rofiling the code. The maximum heap size and the proportion of time spent on garb a e collection is also greater in the Haskell version. In some cases, garbag e collection accounts for almost 50% of the total execution time.
12974717
A System for Learning Routes and Schedules with Genetic Algorithms