Space Search Algorithm Research Articles

On the VC-dimension and boolean functions with long runs

The Vapnik-Chervonenkis (VC) dimension and the Sauer-Shelah lemma have found applications in numerous areas including set theory, combinatorial geometry, graph theory and statistical learning theory. Estimation of the complexity of discrete structures associated with the search space of algorithms often amounts to estimating the cardinality of a simpler class which is effectively induced by some restrictive property of the search. In this paper we study the complexity of Boolean-function classes of finite VC-dimension which satisfy a local ‘smoothness’ property expressed as having long runs of repeated values. As in Sauer’s lemma, a bound is obtained on the cardinality of such classes.

突然変異率を考慮した探索領域対数型GAによるMPSSの設計

This paper presents a new genetic algorithm Search Space Logarithm Type Genetic Algorithm Considering Mutation Rate: for optimization control problems of the Multi-input Power System Stabilizer (MPSS) design. Through the MSSLGA, in general, all the search space of tuning parameters for MPSS can be presumed in the range of 0.01˜100.0. First, this paper proposes developing it into the logarithm to cover the large scale disadvantage. Second, it is presented that mutation rates are decreased together gradually with the generation from the big value. The MSSLGA is possible to obtain the higher-fitness answer easily than the conventional Genetic Algorithm using a constant mutation rate. It is applied to the optimum design of MPSS using Series Compensation Method in power system, which contains low-frequency oscillations, and the efficiency through the MSSLGA is superior to conventional one.

Analysis of SVM regression bounds for variable ranking

This paper addresses the problem of variable ranking for support vector regression. The ranking criteria that we proposed are based on leave-one-out bounds and some variants and for these criteria we have compared different search-space algorithms: recursive feature elimination and scaling factor optimization based on gradient-descent. All these algorithms have been compared on toy problems and real-world QSAR data sets. Results show that the radius-margin criterion is the most efficient criterion for ranking variables. Using this criterion can then lead to support vector regressor with improved error rate while using fewer variables. Our results also support the evidence that gradient-descent algorithm achieves a better variable ranking compared to backward algorithm.

ADAPTIVE WEIGHTED PARTICLE SWARM MULTIOBJECTIVE OPTIMISATION AND SOCIETAL REASONING FOR THE DESIGN OF ALLOY STEELS

A recent shift in optimisation research suggests two trends: one is associated with a move to multi-objective optimisation where challenges of non-unique solutions and the concept of feasibility lie, while the other relates to the development of 'intelligent' algorithms which draw their philosophies from nature and biological intricacies. In this spirit, we propose two new multi-objective optimisation algorithms, a modified PSO algorithm and a societal reasoning method named Reduced Space Searching Algorithm. This paper shows how these algorithms compare to other algorithms and how they can be used to solve a real-world problem, the multi-objective optimal design of alloy steels.

Picking alignments from (Steiner) trees.

The application of Needleman-Wunsch alignment techniques to biological sequences is complicated by two serious problems when the sequences are long: the running time, which scales as the product of the lengths of sequences, and the difficulty in obtaining suitable parameters that produce meaningful alignments. The running time problem is often corrected by reducing the search space, using techniques such as banding, or chaining of high-scoring pairs. The parameter problem is more difficult to fix, partly because the probabilistic model, which Needleman-Wunsch is equivalent to, does not capture a key feature of biological sequence alignments, namely the alternation of conserved blocks and seemingly unrelated nonconserved segments. We present a solution to the problem of designing efficient search spaces for pair hidden Markov models that align biological sequences by taking advantage of their associated features. Our approach leads to an optimization problem, for which we obtain a 2-approximation algorithm, and that is based on the construction of Manhattan networks, which are close relatives of Steiner trees. We describe the underlying theory and show how our methods can be applied to alignment of DNA sequences in practice, successfully reducing the Viterbi algorithm search space of alignment PHMMs by three orders of magnitude.

Search space reduction in QoS routing

To provide real-time service or engineer constrained-based paths, networks require the underlying routing algorithm to be able to find low-cost paths that satisfy given quality-of-service constraints. However, the problem of constrained shortest (least-cost) path routing is known to be NP-hard, and some heuristics have been proposed to find a near-optimal solution. However, these heuristics either impose relationships among the link metrics to reduce the complexity of the problem which may limit the general applicability of the heuristic, or are too costly in terms of execution time to be applicable to large networks. In this paper, we focus on solving the delay-constrained minimum-cost path problem, and present a fast algorithm to find a near-optimal solution. This algorithm, called delay-cost-constrained routing (DCCR), is a variant of the k-shortest-path algorithm. DCCR uses a new adaptive path weight function together with an additional constraint imposed on the path cost, to restrict the search space. Thus, DCCR can return a near-optimal solution in a very short time. Furthermore, we use a variant of the Lagrangian relaxation method proposed by Handler and Zang [Networks 10 (1980) 293] to further reduce the search space by using a tighter bound on path cost. This makes our algorithm more accurate and even faster. We call this improved algorithm search space reduction + DCCR (SSR + DCCR). Through extensive simulations, we confirm that SSR + DCCR performs very well compared to the optimal but very expensive solution.

Face posture estimation using eigen analysis on an IBR (image based rendered) database

In this paper, we present a novel representation of the human face for estimating the orientation of the human head in a two dimensional intensity image. The method combines the use of the much familiar eigenvalue based dissimilarity measure with image based rendering. There are two main components of the algorithm described here: the offline hierarchical image database generation and organization, and the online pose estimation stage. The synthetic images of the subject's face are automatically generated offline, for a large set of pose parameter values, using an affine coordinate based image reprojection technique. The resulting database is formally called as the IBR (or image based rendered) database. This is followed by the hierarchical organization of the database, which is driven by the eigenvalue based dissimilarity measure between any two synthetic image pair. This hierarchically organized database is a detailed, yet structured, representation of the subject's face. During the pose estimation of a subject in an image, the eigenvalue based measure is invoked again to search the synthetic (IBR) image closest to the real image. This approach provides a relatively easy first step to narrow down the search space for complex feature detection and tracking algorithms in potential applications like virtual reality and video-teleconferencing applications.

Side-chain flexibility in proteins upon ligand binding.

Ligand binding may involve a wide range of structural changes in the receptor protein, from hinge movement of entire domains to small side-chain rearrangements in the binding pocket residues. The analysis of side chain flexibility gives insights valuable to improve docking algorithms and can provide an index of amino-acid side-chain flexibility potentially useful in molecular biology and protein engineering studies. In this study we analyzed side-chain rearrangements upon ligand binding. We constructed two non-redundant databases (980 and 353 entries) of "paired" protein structures in complexed (holo-protein) and uncomplexed (apo-protein) forms from the PDB macromolecular structural database. The number and identity of binding pocket residues that undergo side-chain conformational changes were determined. We show that, in general, only a small number of residues in the pocket undergo such changes (e.g., approximately 85% of cases show changes in three residues or less). The flexibility scale has the following order: Lys > Arg, Gln, Met > Glu, Ile, Leu > Asn, Thr, Val, Tyr, Ser, His, Asp > Cys, Trp, Phe; thus, Lys side chains in binding pockets flex 25 times more often then do the Phe side chains. Normalizing for the number of flexible dihedral bonds in each amino acid attenuates the scale somewhat, however, the clear trend of large, polar amino acids being more flexible in the pocket than aromatic ones remains. We found no correlation between backbone movement of a residue upon ligand binding and the flexibility of its side chain. These results are relevant to 1. Reduction of search space in docking algorithms by inclusion of side-chain flexibility for a limited number of binding pocket residues; and 2. Utilization of the amino acid flexibility scale in protein engineering studies to alter the flexibility of binding pockets.

Spatial Data Mining: Database Primitives, Algorithms and Efficient DBMS Support

Spatial data mining algorithms heavily depend on the efficient processing of neighborhood relations since the neighbors of many objects have to be investigated in a single run of a typical algorithm. Therefore, providing general concepts for neighborhood relations as well as an efficient implementation of these concepts will allow a tight integration of spatial data mining algorithms with a spatial database management system. This will speed up both, the development and the execution of spatial data mining algorithms. In this paper, we define neighborhood graphs and paths and a small set of database primitives for their manipulation. We show that typical spatial data mining algorithms are well supported by the proposed basic operations. For finding significant spatial patterns, only certain classes of paths “leading away” from a starting object are relevant. We discuss filters allowing only such neighborhood paths which will significantly reduce the search space for spatial data mining algorithms. Furthermore, we introduce neighborhood indices to speed up the processing of our database primitives. We implemented the database primitives on top of a commercial spatial database management system. The effectiveness and efficiency of the proposed approach was evaluated by using an analytical cost model and an extensive experimental study on a geographic database.

An integrated process planning system using feature reasoning and space search-based optimization

In this paper we describe the architecture of a new integrated process planning system, called 31-PP, which is comprised of three modules: feature completion, process selection, and process sequencing. We have applied a knowledge-based approach to feature completion and process selection, and the space search algorithm for process sequencing. The resulting process planning system provides for both upstream and downstream integration of CAPP with CAD and scheduling systems, rej.pectively. Description of the methodologies and procedures is provided and some experimental results that demonstrate the system performance are presented.

A routing strategy based on genetic algorithms

Circuit routing is a problem of searching the shortest path and it is solved using high complexity algorithms. The time and space requirements of these algorithms depend linearly on search space. Genetic algorithms (GAs) are tough algorithms of adaptive search that have proved to be an efficient tool in optimization and complex search processes. The goal of our work is to improve a classical routing algorithm (Lee's algorithm) using a GA. This GA helps to reduce Lee's algorithm search space and, as a consequence, the spatial and temporal complexities of Lee's algorithm have been reduced. The algorithm we propose, GALO (genetic algorithm for Lee's algorithm optimization), is the combination of our GA and Lee's algorithm. GALO obtains, if it exists, the shortest path between the circuit nodes that we are interconnecting, while overcoming the GAs drawback: finding quasi-solutions. We have accomplished a theoretical study of the worst case temporal complexity of our GA and GALO, having three main goals: first, comparing the complexity of our solution with the Lee's classical solution complexity; second, noticing the relevance that this study has in designing and debugging genetic algorithms; and finally, comparing theoretical and experimental results through several examples.

Cluster conformational analysis of a seco acid used in Woodward's total synthesis of erythromycin A

An efficient conformational space search algorithm CONFLEX augmented with a clustering technique was applied to a model of that particular seco acid which successfully cyclized to macrocyclic lactone in Woodward's total synthesis of erythromycin A, with the purpose of finding the folded conformation long supposed to exist in this seco acid. Among 4999 energy minima that have been generated, 270 of them had conformations almost superimposable with those of the dominant forms of the cyclized model, but the combined population of the reactive conformers was only 3.2% of the total. The implications of these observations are discussed.

Time-efficient state space search

We present two time-efficient state space algorithms for searching minimax trees. Because they are based on SSS∗ and Dual∗, both dominate Alpha-Beta on a node count basis. Moreover, one of them is always faster in searching random trees, even when the leaf node evaluation time is negligible. The fast execution time is attributed to the recursive nature of our algorithms and to their efficient data structure (a simple array) for storing the best-first node information. In practical applications with more expensive leaf evaluations we conjecture that the recursive state space search algorithms perform even better and might eventually supersede the popular directional search methods.

PROPOSED MODIFICATIONS TO PARALLEL STATE SPACE SEARCH OF GAME TREES

Modifications have been proposed to state space search algorithms for pruning game trees in parallel.The performance of these modified algorithms is empirically shown to be better than the other known parallel pruning algorithms.

A topological stereo matcher

Presented here is a new stereo algorithm that produces dense, high-quality, subpixel disparity maps. It offers two improvements over previous algorithms. First, it does not blur disparity values across sharp changes in depth. Second, it can reconstruct the correct correspondence between two images even when there is substantial vertical displacement between them: this algorithm has been tested with rotations up to 10 degrees and vertical translations up to 16 pixels. Although such image pairs require extra processing time, this ability is vital when exact calibration cannot be maintained. The new algorithm depends on two new ideas. First, it exploits the fact that the correct vertical disparity field is due to camera misalignment and, thus, has only a few (significant) degrees of freedom. The algorithm passes camera alignment parameters, not raw disparity fields, between scales. Disparities at individual locations can diverge only slightly from this global model, greatly reducing the algorithm's search space. Second, the new algorithm uses a pre-match filter that prevents two patches of image from matching if they do not have the same (local) topological structure. This constraint subsumes previous “figural continuity” proposals and can be checked by simple, local operations. The filter seems to improve the algorithm's ability to select the correct match from many alternatives and it suppresses intermediate values near sharp changes in disparity. This technique can be extended to other matching tasks, such as motion tracking, analyzing texture periodicity, and evaluating the performance of edge finders.

Problem Reduction Representation for the Linguistic Analysis of Waveforms

This paper shows how the nondirectional structural analysis of pattern data can be performed by matching a problem reduction representation (PRR) of pattern structure with sample data, using a best-first state space search algorithm called SSS*. The end result of the matching algorithm is a tree whose nodes represent recognized structures in the data. Tip nodes of the tree structure correspond to primitives which are recognized in the raw data by curve fitting routines. The operators of the algorithm allow the tree to be constructed with a combination of top-down or bottom-up steps. The matching of the structure tree to waveform segments need not be done in a left-right sequence. Moreover ambiguous matches are pursued in a best first order by using state space search with partial parse trees as states. A software system called WAPSYS (for waveform parsing system) is described, which implements this structural analysis paradigm. Experience using WAPSYS to analyze carotid pulse waves is also discussed.

Searching and gradualness

Consider the elements of the power set S as the alternatives in a search space. Each element of the set has a value and the goal of a search is to find an element with near-minimum value. If high-valued elements of cardinality i (recall that elements of S are themselves sets) are subsets of high-valued elements of cardinality i + 1, then the search space has a king of gradualness that should facilitate search. A search algorithm might generate a series, S′(1), … S′( k) of samples in S, where all the elements in S′ ( i) have cardinality i. I propose a class of search algorithms, where each algorithm A( j) generates S′( i + 1) by emphasizing the j lowest-valued elements in S′( i). I then define search space gradualness and search algorithm performance and formally relate gradualness and performance.