8882088
Design of Nearest Neighbor Classifiers Using an Intelligent Multi-objective Evolutionary Algorithm
The goal of designing optimal nearest neighbor classifiers is to maximize classification accuracy while minimizing the sizes of both reference and feature sets. A usual way is to adaptively weight the three objectives as an objective function and then use a single-objective optimization method for achieving this goal. This paper proposes a multi-objective approach to cope with the weight tuning problem for practitioners. A novel intelligent multi-objective evolutionary algorithm IMOEA is utilized to simultaneously edit compact reference and feature sets for nearest neighbor classification. Two comparison studies are designed to evaluate performance of the proposed approach. It is shown empirically that the IMOEA-designed classifiers have high classification accuracy and small sizes of reference and feature sets. Moreover, IMOEA can provide a set of good solutions for practitioners to choose from in a single run. The simulation results indicate that the IMOEA-based approach is an expedient method to design nearest neighbor classifiers, compared with an existing single- objective approach.
5744211
Intelligent evolutionary algorithms for large parameter optimization problems
This work proposes two intelligent evolutionary algorithms IEA and IMOEA using a novel intelligent gene collector (IGC) to solve single and multiobjective large parameter optimization problems, respectively. IGC is the main phase in an intelligent recombination operator of IEA and IMOEA. Based on orthogonal experimental design, IGC uses a divide-and-conquer approach, which consists of adaptively dividing two individuals of parents into N pairs of gene segments, economically identifying the potentially better one of two gene segments of each pair, and systematically obtaining a potentially good approximation to the best one of all combinations using at most 2N fitness evaluations. IMOEA utilizes a novel generalized Pareto-based scale-independent fitness function for efficiently finding a set of Pareto-optimal solutions to a multiobjective optimization problem. The advantages of IEA and IMOEA are their simplicity, efficiency, and flexibility. It is shown empirically that IEA and IMOEA have high performance in solving benchmark functions comprising many parameters, as compared with some existing EAs.
26523507
MeSwarm: memetic particle swarm optimization
In this paper, a novel variant of particle swarm optimization (PSO), named memetic particle swarm optimization algorithm (MeSwarm), is proposed for tackling the overshooting problem in the motion behavior of PSO. The overshooting problem is a phenomenon in PSO due to the velocity update mechanism of PSO. While the overshooting problem occurs, particles may be led to wrong or opposite directions against the direction to the global optimum. As a result, MeSwarm integrates the standard PSO with the Solis and Wets local search strategy to avoid the overshooting problem and that is based on the recent probability of success to efficiently generate a new candidate solution around the current particle. Thus, six test functions and a real-world optimization problem, the flexible protein-ligand docking problem are used to validate the performance of MeSwarm. The experimental results indicate that MeSwarm outperforms the standard PSO and several evolutionary algorithms in terms of solution quality.
6409733
Routing in an Internet-scale network emulator
One of the primary challenges facing scalable network emulation and simulation is the overhead of storing network-wide routing tables or computing appropriate routes on a per-packet basis. We present an approach to routing table calculation and storage based on spanning tree construction that provides an order of magnitude reduction in routing table size for Internet-like topologies. In our approach, we maintain a variable number of spanning trees for a given topology and choose the path between two hosts in each tree, choosing the shortest. We also populate offline a negative cache of actual shortest paths for source-destination pairs - typically a few percent of the total - where the lookups result in sub-optimal routes. We have implemented our technique in a popular network emulator, ModelNet, and show that our enhanced version can emulate Internet topologies 10-100 times larger than previously possible.
14208339
Using Nondeterminism to Design Efficient Deterministic Algorithms
Abstract
In this paper we illustrate how nondeterminism can be used
conveniently and effectively in designing efficient deterministic
algorithms. In particular, our method gives a parameterized
algorithm of running time O((5.7 k)k n) for the 3-D matching
problem, which significantly improves the previous algorithm by
Downey et al. The algorithm can be generalized to
yield an improved algorithm for the r-D matching problem for any
positive integer r. The method can also be employed in designing
deterministic algorithms for other optimization problems as well.


8551249
Functional census of mutation sequence spaces: the example of p53 cancer rescue mutants
Many biomedical problems relate to mutant functional properties across a sequence space of interest, e.g., flu, cancer, and HIV. Detailed knowledge of mutant properties and function improves medical treatment and prevention. A functional census of p53 cancer rescue mutants would aid the search for cancer treatments from p53 mutant rescue. We devised a general methodology for conducting a functional census of a mutation sequence space by choosing informative mutants early. The methodology was tested in a double-blind predictive test on the functional rescue property of 71 novel putative p53 cancer rescue mutants iteratively predicted in sets of three (24 iterations). The first double-blind 15-point moving accuracy was 47 percent and the last was 86 percent; r = 0.01 before an epiphanic 16th iteration and r = 0.92 afterward. Useful mutants were chosen early (overall r = 0.80). Code and data are freely available (http://www.igb.uci.edu/research/research.html, corresponding authors: R.H.L. for computation and R.K.B. for biology)
46388294
Soft Sorting in Logic Programming
Integrating sorts (types) into logic programming languages increases their expressiveness and evaluation efficiency. The sort structures usually considered are strict, in the sense that whenever an expression has a subexpression which is not properly sorted, the expression is classified as ill-sorted, and in effect is regarded as meaningless. Typically a logic programming language with strict sorting is incomplete, in the sense that some intuitively meaningful programs and queries are ill-sorted and hence are not part of the language.
8876890
Overlay mesh construction using interleaved spanning trees
In this paper we evaluate a method of using interleaved spanning trees to compose a resilient, high performance overlay mesh. Though spanning trees of arbitrary type could be used to construct an overlay mesh, we focus on a distributed algorithm that computes k minimum spanning trees on an arbitrary graph. The principal motivation behind this strategy is to provide applications with a k-redundant, high quality mesh suitable for demanding applications like A/V broadcast, video conferencing, data collection, multi-path routing, and file mirroring/transfer. We elaborate details of k-MST, pointing out advantages and potential problem points of the protocol, and then analyze its performance using a variety of metrics with simulation as well as a functional PlanetLab implementation.