Search Algorithm

We have performed a search for bursts of gravitational waves associated with the very bright Gamma Ray Burst GRB030329, using the two detectors at the LIGO Hanford Observatory. Our search covered the most sensitive frequency range of the LIGO detectors (approximately 80-2048 Hz), and we specifically targeted signals shorter than ≃150 ms. Our search algorithm looks for excess correlated power between the two interferometers and thus makes minimal assumptions about the gravitational waveform. We observed no candidates with gravitational wave signal strength larger than a pre-determined threshold. We report frequency dependent upper limits on the strength of the gravitational waves associated with GRB030329. Near the most sensitive frequency region, around ≃250 Hz, our root-sum-square (RSS) gravitational wave strain sensitivity for optimally polarized bursts was better than hRSS≃6×10 −21 Hz −1/2 . Our result is comparable to the best published results searching for association between gravitational waves and GRBs.

To uncover qualitative and quantitative patterns in a data set is a challenging task for research in the area of machine learning and data analysis. Due to the complexity of real-world data, high-order (polythetic) patterns or event associations, in addition to first-order class-dependent relationships, have to be acquired. Once the patterns of different orders are found, they should be represented in a form appropriate for further analysis and interpretation. In this paper, we propose a novel method to discover qualitative and quantitative patterns (or event associations) inherent in a data set. It uses the adjusted residual analysis in statistics to test the significance of the occurrence of a pattern candidate against its expectation. To avoid exhaustive search of all possible combinations of primary events, techniques of eliminating the impossible pattern candidates are developed. The detected patterns of different orders are then represented in an attributed hypergraph which is lucid for pattern interpretation and analysis. Test results on artificial and real-world data are discussed toward the end of the paper.

The selection of entries to be included/excluded in Branch and Bound algorithms is usually done on the basis of cost values. We consider the class of Depth First Search algorithms, and we propose to use upper tolerances to guide the search for optimal solutions. In spite of the fact that it needs time to calculate tolerances, our computational experiments for Asymmetric Traveling Salesman Problems show that in most situations tolerance-based algorithms outperform cost-based algorithms. The solution time reductions are mainly caused by the fact that the branching process becomes much more effective, so that optimal solutions are found in an earlier stage of the branching process. The use of tolerances also reveals why the widely used choice for branching on a smallest cycle in assignment solutions is on average the most effective one. Moreover, it turns out that tolerance-based DFS algorithms are better in solving difficult instances than the Best First Search algorithm from Carpaneto et al. [Carpaneto, G., Dell'Amico, M., Toth, P., 1995. Exact solution of large-scale asymmetric traveling salesman problems. ACM Transactions on Mathematical Software 21 (4), 394-409].

We report on a case study to assess the use of an advanced knowledge-based software design technique with programmers who have not participatedin the technique's development. We use the KIDS approach to algorithm design to construct two global search algorithms that route baggage through a transportation net. Construction of the second algorithm involves extending the KIDS knowledge base. Experience with the case study leads us to integrate the approach with the spiral and prototyping models of software engineering, and to discuss ways to deal with incomplete design knowledge.

A major goal for researchers in neuroevolution is to evolve artificial neural networks (ANNs) that can learn during their lifetime. Such networks can adapt to changes in their environment that evolution on its own cannot anticipate. However, a profound problem with evolving adaptive systems is that if the impact of learning on the fitness of the agent is only marginal, then evolution is likely to produce individuals that do not exhibit the desired adaptive behavior. Instead, because it is easier at first to improve fitness without evolving the ability to learn, they are likely to exploit domaindependent static (i.e. non-adaptive) heuristics. This paper proposes a way to escape the deceptive trap of static policies based on the novelty search algorithm, which opens up a new avenue in the evolution of adaptive systems because it can exploit the behavioral difference between learning and nonlearning individuals. The main idea in novelty search is to abandon objective-based fitness and instead simply search only for novel behavior, which avoids deception entirely and has shown prior promising results in other domains. This paper shows that novelty search significantly outperforms fitness-based search in a tunably deceptive T-Maze navigation domain because it fosters the emergence of adaptive behavior.

Pure adaptive seach iteratively constructs a sequence of interior points uniformly distributed within the corresponding sequence of nested improving regions of the feasible space. Tbat is, at any iteration, the hext point in the sequence is uniformly distributed over the region of feasible space containing all points that are strictly superior in value to the previous points in the sequence. The complexity of this algorithm is measured by the expected number of iterations required to achieve a given accuracy of solution. We show that for global mathematical programs satisfying the Lipschitz condition, its complexity increases at most linearly in the dimension of the problem.

Traditional single-agent search algorithms usually make simplifying assumptions (single search agent, stationary target, complete knowledge of the state, and sufficient time). There are algorithms for relaxing one or two of these constraints; in this paper we want to relax all four. The application domain is to have multiple search agents cooperate to pursue and capture a moving target. Agents are allowed to communicate with each other. For solving Multiple Agents Moving Target (MAMT) applications, we present a framework for specifying a family of suitable search algorithms. This paper investigates several effective approaches for solving problem instances in this domain.

... 870 JZC Lai Image and Vision Computing 15 (1997) 867871 Table 2 The execution time of enci ading a real image (tiffany) for 3 different algorithms' Number of codewords Available encoding algorithm (seconds) EAWFC (seconds) E c AWFS with = 3 (seconds) Speedup ...

In this paper, a novel quantum swarm evolutionary algorithm (QSE) is presented based on the quantum-inspired evolutionary algorithm (QEA). A new definition of Q-bit expression called quantum angle is proposed, and an improved particle swarm optimization (PSO) is employed to update the quantum angles automatically. The simulated results in solving 0-1 knapsack problem show that QSE is superior to traditional QEA. In addition, the comparison experiments show that QSE is better than many traditional heuristic algorithms, such as climb hill algorithm, simulation anneal algorithm and taboo search algorithm. Meanwhile, the experimental results of 14 cities traveling salesman problem (TSP) show that it is feasible and effective for small-scale TSPs, which indicates a promising novel approach for solving TSPs. r

A robot has to construct a complete map of an unknown environment modeled as an undirected connected graph. The task is to explore all nodes and edges of the graph using the minimum number of edge traversals. The penalty of an exploration Ž Ž. Ž .. algorithm running on a graph G s V G , E G is the worst-case number of < Ž .< traversals in excess of the lower bound E G that it must perform in order to explore the entire graph. We give an exploration algorithm whose penalty is Ž< Ž .<. O V G for every graph. We also show that some natural exploration algorithms cannot achieve this efficiency.

We introduce the concept of “minimal” search algorithm for a set of functions to optimize. We investigate the structure of closed under permutation (c.u.p.) sets and we calculate the performance of an algorithm applied to them. We prove that each set of functions based on the distance to a given optimal solution, among which trap functions, onemax or the recently introduced onemix functions, and the NK-landscapes are not c.u.p. and thus the thesis of the sharpened No Free Lunch Theorem does not hold for them. Thus, it makes sense to look for a specific algorithm for those sets. Finally, we propose a method to build a “good” (although not necessarily minimal) search algorithm for a specific given set of problems. The algorithms produced with this technique show better average performance than a genetic algorithm executed on the same set of problems, which was expected given that those algorithms are problem-specific. Nevertheless, in general they cannot be applied for real-life problems, given their high computational complexity that we have been able to estimate.

that use space linear in the search depth are widely employed in practice to solve difficult problems optimally, such as planning and scheduling. In this paper, we study the average-case performance of linear-space search algorithms, including depth-first branch-andbound (DFBnB), iterative-deepening (ID), and recursive best-first search (RBFS). To facilitate our analyses, we use a random tree T( b, d) that has mean branching factor b, depth d, and node costs that are the sum of the costs of the edges from the root to the nodes. We prove that the expected number of nodes expanded by DFBnB on a random tree is no more than bd times the expected number of nodes expanded by best-first search (BFS) on the same tree, which usually requires space that is exponential in depth d. We also show that DFBnB is asymptotically optimal when BFS runs in exponential time, and ID and RBFS are asymptotically optimal when the edge costs of T( b,d) are integers. If bpo is the expected number of children of a node whose costs are the same as that of their parent, then the expected number of nodes expanded by these three linear-space algorithms is exponential when bpo < 1, at most 0( d4) when bpo = 1, and at most quadratic when bpo > 1. In addition, we study the heuristic branching factor of T( b, d) and the effective branching factor of BFS, DFBnB, ID, and RBFS on T( b, d). Furthermore, we use our analytic results to explain a surprising anomaly in the performance of these algorithms, and to predict the existence of a complexity transition in the Asymmetric Traveling Salesman Problem.

Available in: http://www.redalyc.org/src/inicio/ArtPdfRed.jsp?iCve=43019328016 ... Redalyc Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and ... A harmony search algorithm for clustering with feature selection ...

In this paper the MRL rescue robot team and its robots are explained. We have designed and built three new robots including two autonomous indoor robots and one out-door (rough terrain) robot for different situations/arenas. Our main goal of this activity is to achieve a practical rescue robot for real situation such as earthquake which is quite common in our

Previous work in Query-by-Humming systems has left open many questions. Although a variety of techniques have been explored, there has been relatively little work to compare them under controlled conditions, especially with "real" audio queries from human subjects. Previous work comparing note-interval matching, melodic contour matching, and HMM-based matching is extended with comparisons to the Phillips CubyHum algorithm and various n-gram search algorithms. We also explore the sensitivity of note-interval dynamic programming searches to different parameters and consider two-stage searches combining a fast n-gram search with a more precise but slower dynamic programming algorithm.

In this paper we introduce COV, a novel information retrieval (IR) algorithm for massive databases based on vector space modeling and spectral analysis of the covariance matrix, for the document vectors, to reduce the scale of the problem. Since the dimension of the covariance matrix depends on the attribute space and is independent of the number of documents, COV can be applied to databases that are too massive for methods based on the singular value decomposition of the document-attribute matrix, such as latent semantic indexing (LSI). In addition to improved scalability, theoretical considerations indicate that results from our algorithm tend to be more accurate than those from LSI, particularly in detecting subtle differences in document vectors. We demonstrate the power and accuracy of COV through an important topic in data mining, known as outlier cluster detection. We propose two new algorithms for detecting major and outlier clusters in databases—the first is based on LSI, and the second on COV. Our implementation studies indicate that our cluster detection algorithms outperform the basic LSI and COV algorithm in detecting outlier clusters.

Smith et al. report a large study of the accuracy of 38 search procedures for recovering effective connections in simulations of DCM models under 28 different conditions. Their results are disappointing: no method reliably finds and directs connections without large false negatives, large false positives, or both. Using multiple subject inputs, we apply a previously published search algorithm, IMaGES, and novel orientation algorithms, LOFS, in tandem to all of the simulations of DCM models described by Smith et al. (2011). We find that the procedures accurately identify effective connections in almost all of the conditions that Smith et al. simulated and, in most conditions, direct causal connections with precision greater than 90% and recall greater than 80%.

In this paper we address the problem of building an effi-cient database management system that allows querying for similar images. This system, which supports image-based queries on a database of photographs taken around the campus, helps the user to identify his/her ...

In this paper we present a tabu search algorithm for the min-max k-Chinese postman problem (MM k-CPP). Given an undirected edge-weighted graph and a distinguished depot node, the MM k-CPP consists of finding k > 1 tours (starting and ending at the depot node) such that each edge is traversed by at least one tour and the length of the longest tour is minimized. A special emphasis is put on investigating the trade-off between running time effort and solution quality when applying different improvement procedures in the course of the neighborhood construction. Furthermore, different neighborhoods are analyzed. Extensive computational results show that the tabu search algorithm outperforms all known heuristics and improvement procedures. Scope and purpose Given a road network, the Chinese postman problem (CPP) is to find the shortest postman tour covering all the roads in the network. Applications of the CPP include road maintenance, garbage collection, mail delivery, etc. Since usually large road networks have to be serviced the work load must be distributed among k 2 vehicles. In contrast to the usual objective to minimize the total distance traveled by the k vehicles (k-CPP), for the min-max k-Chinese postman problem (MM k-CPP) the aim is to minimize the length of the longest of the k tours. This kind of objective is preferable when customers have to be served as early as possible. Furthermore, tours will be enforced to be more balanced resulting in a fair scheduling of tours. Although the CPP and the k-CPP are polynomially solvable, the MM k-CPP is NP-hard. Hence, we must rely on heuristics producing approximate solutions. The purpose of this paper is to present a tabu search algorithm for the MM k-CPP which outperforms all known heuristics. In many cases we obtained solutions which could be proved to be near-optimal or even optimal.

With ever increasing digital data in form of files, there is a need of efficient text searching algorithms with their applications. This paper describes an algorithm for searching any sentence in the given pool of files. The efficiency of the algorithm depends on the fact about sentences in any language that, there can be no such condition where a sentence may be a part of some similar sentence (as in case of patterns). Keeping this property of the languages in mind, we are presenting a linear order search algorithm for sentences with no preprocessing time.

An algorithm for detecting and analyzing potential enroute conflicts has been designed and implemented within the Center-TRACON Automation System (CTAS). The design uses the 4D trajectories provided by CTAS to produce a set of probable future conflicts, and assists the enroute sector controller in efficiently resolving these conflicts. Conflicts are detected via comparisons of trajectories at closely spaced time instants, with measures taken to limit the computations required to complete the search. Performance tests indicate more than 800 aircraft can be processed by the conflict detection and analysis algorithm within a search cycle of 10 seconds. This suggests that the search algorithm easily meets the performance requirements for an automated conflict detection and resolution tool in the current air traffic system. The algorithm includes a trial resolution functionality which automatically detects conflicts o f proposed resolutions, and gives near instantaneous feedback to controller input. Field evaluation of the Conflict Probe will be conducted at the Denver and Fort Worth Air Route Traffic Control Centers (ARTCC) beginning in September, 1997.

The goal of this study is to obtain globally optimum shapes for two-dimensional structures subject to quasi-static loads and restraints. For this purpose a technique is proposed, using which the volume (or weight) of a structure can be minimized. The emphasis is on how one can define the shape precisely, and find a shape that accurately reflects the globally optimum shape.

As design constraints, stresses developed in the structure should not exceed the maximum allowable stress, and connectivity of the structure should not be lost during shape changes. Optimization is achieved by a stochastic search algorithm called direct simulated annealing (DSA), which seeks the global minimum through randomly generated configurations. In order to obtain random configurations, a boundary variation technique is used. In this technique, a set of key points is chosen and connected by cubic splines to describe the boundary of the structure. Whenever the positions of the key points are changed in random directions, a new shape is obtained. Thus, coordinates of the key points serve as design variables. In order to apply the optimization procedure, a general computer code was developed using ANSYS Parametric Design Language. A number of cases were examined to test its effectiveness. The results show that this technique can be applied to two-dimensional shape optimization problems with high reliability even for cases where the entire free boundary is allowed to vary.

This paper develops the multidimensional binary search tree (or k -d tree, where k is the dimensionality of the search space) as a data structure for storage of information to be retrieved by associative searches. The k -d tree is defined and examples are given. It is shown to be quite efficient in its storage requirements. A significant advantage of this structure is that a single data structure can handle many types of queries very efficiently. Various utility algorithms are developed; their proven average running times in an n record file are: insertion, O (log n ); deletion of the root, O ( n ( k -1)/ k ); deletion of a random node, O (log n ); and optimization (guarantees logarithmic performance of searches), O ( n log n ). Search algorithms are given for partial match queries with t keys specified [proven maximum running time of O ( n ( k - t )/ k )] and for nearest neighbor queries [empirically observed average running time of O (log n ).] These performances far surpass the b...

Distribution centers location problem is concerned with how to select distribution centers from the potential set so that the total relevant cost is minimized. This paper mainly investigates this problem under fuzzy environment. Consequentially, chanceconstrained programming model for the problem is designed and some properties of the model are investigated. Tabu search algorithm, genetic algorithm and fuzzy simulation algorithm are integrated to seek the approximate best solution of the model. A numerical example is also given to show the application of the algorithm.

Cancer leads to approximately 25% of all mortalities, making it the second leading cause of death in the United States. Early and accurate detection of cancer is critical to the well being of patients. Analysis of gene expression data leads to cancer identification and classification, which will facilitate proper treatment selection and drug development. Gene expression data sets for ovarian, prostate, and lung cancer were analyzed in this research. An integrated gene-search algorithm for genetic expression data analysis was proposed. This integrated algorithm involves a genetic algorithm and correlation-based heuristics for data preprocessing (on partitioned data sets) and data mining (decision tree and support vector machines algorithms) for making predictions. Knowledge derived by the proposed algorithm has high classification accuracy with the ability to identify the most significant genes. Bagging and stacking algorithms were applied to further enhance the classification accuracy. The results were compared with that reported in the literature. Mapping of genotype information to the phenotype parameters will ultimately reduce the cost and complexity of cancer detection and classification. ᭧

In the past, many methods have been proposed for the inference of probabilistic and non-probabilistic finite state automata from positive examples of their behaviour. In this paper, we introduce a search method guided by the reformation-theoretic Minimum Message Length principle to infer Probabilistic Finite State Automata (PFSA). ~ The method is a beam search technique that searches for the best PFSA that accounts {or a given dataset. Results of testing this method against some earlier algorithms are presented. A simulated annealing version of the beam search algorithm is also described as ongoing research in the area.

This paper proposed a penalty guided artificial bee colony algorithm (ABC) to solve the reliability redundancy allocation problem (RAP). The redundancy allocation problem involves setting reliability objectives for components or subsystems in order to meet the resource consumption constraint, e.g. the total cost. RAP has been an active area of research for the past four decades. The difficulty that one is confronted with the RAP is the maintenance of feasibility with respect to three nonlinear constraints, namely, cost, weight and volume related constraints. In this paper nonlinearly mixed-integer reliability design problems are investigated where both the number of redundancy components and the corresponding component reliability in each subsystem are to be decided simultaneously so as to maximize the reliability of the system. The reliability design problems have been studied in the literature for decades, usually using mathematical programming or heuristic optimization approaches. To the best of our knowledge the ABC algorithm can search over promising feasible and infeasible regions to find the feasible optimal/near-optimal solution effectively and efficiently; numerical examples indicate that the proposed approach performs well with the reliability redundant allocation design problems considered in this paper and computational results compare favorably with previously-developed algorithms in the literature.

Abstract—There are many classical algorithms for finding routing in FPGA. But Using DNA computing we can solve the routes efficiently and fast. The run time complexity of DNA algorithms is much less than other classical algorithms which are used for solving routing in FPGA. The ...

Ray tracing is a well known method for photorealistic image synthesis, volume visualization and rendering. Over the last decade the method is being adopted throughout the research community around the world. With the advent of the high speed processing units, the method has been emerging from offline rendering towards real time rendering. The success behind ray tracing algorithms lies in the use of acceleration data structures and modern processing power of CPUs and GPUs. kd-tree is one of the most widely used data structures based on surface area heuristics (SAH). The major bottleneck in kd-tree construction is the time consumed to find optimum split locations. In this paper, we propose a prediction algorithm for animated ray tracing based on Kalman filtering. The algorithm successfully predicts the split locations for the next consecutive frame in the animation sequence. Thus, giving good initial starting points for one dimensional search algorithms to find optimum split locationsin our case parabolic interpolation combined with golden section search. With our technique implemented, we have reduced the "running kd-tree construction" time by between 78% and 87% for dynamic scenes with 16.8K and 252K polygons respectively.

Bandwidth estimation techniques seek to provide an accurate estimation of available bandwidth such that network applications can adjust their behavior accordingly. However, most current techniques were designed for wired networks and produce relatively inaccurate results and long convergence times on wireless networks where capacity can vary dramatically. This paper presents a new Wireless Bandwidth estimation tool, WBest, designed for fast, non-intrusive, accurate estimation of available bandwidth in IEEE 802.11 networks. WBest is a twostage algorithm: 1) a packet pair technique estimates the effective capacity over a flow path where the last hop is a wireless LAN (WLAN); and 2) a packet train technique estimates achievable throughput to infer the available bandwidth. WBest parameters are optimized given the tradeoffs of accuracy, intrusiveness and convergence time. The advantage of WBest stems from avoiding a search algorithm to detect the available bandwidth by statistically detecting the available fraction of the effective capacity to mitigate estimation delay and the impact of random wireless channel errors. WBest is implemented and evaluated on an 802.11 wireless testbed. Comparisons with other available bandwidth estimation tools shows WBest to have higher accuracy, lower intrusiveness and faster convergence times. Thus, WBest demonstrates the potential for improving the performance of applications that need bandwidth estimation, such as multimedia streaming, on wireless networks.

The Sloan Digital Sky Survey-II Supernova Survey has identified a large number of new transient sources in a 300 deg 2 region along the celestial equator during its first two seasons of a three-season campaign. Multi-band (ugriz) light curves were measured for most of the sources, which include solar system objects, Galactic variable stars, active galactic nuclei, supernovae (SNe), and other astronomical transients. The imaging survey is augmented by an extensive spectroscopic follow-up program to identify SNe, measure their redshifts, and study the physical conditions of the explosions and their environment through spectroscopic diagnostics. During the survey, light curves are rapidly evaluated to provide an initial photometric type of the SNe, and a selected sample of sources are targeted for spectroscopic observations. In the first two seasons, 476 sources were selected for spectroscopic observations, of which 403 were identified as SNe. For the Type Ia SNe, the main driver for the Survey, our photometric typing and targeting efficiency is 90%. Only 6% of the photometric SN Ia candidates were spectroscopically classified as non-SN Ia instead, and the remaining 4% resulted in low signal-to-noise, unclassified spectra. This paper describes the search algorithm and the software, and the real-time processing of the SDSS imaging data. We also present the details of the supernova candidate selection procedures and strategies for follow-up spectroscopic and imaging observations of the discovered sources.

A new population-based search algorithm called the Bees Algorithm (BA) is presented. The algorithm mimics the food foraging behaviour of swarms of honey bees. In its basic version, the algorithm performs a kind of neighbourhood search combined with random search and can be used for both combinatorial optimisation and functional optimisation. This paper focuses on the latter. Following a description of the algorithm, the paper gives the results obtained for a number of benchmark problems demonstrating the efficiency and robustness of the new algorithm.

Conservation of information (COI) popularized by the no free lunch theorem is a great leveler of search algorithms, showing that on average no search outperforms any other. Yet in practice some searches appear to outperform others. In consequence, some have questioned the significance of COI to the performance of search algorithms. An underlying foundation of COI is Bernoulli's Principle of Insufficient Reason 1 (PrOIR) which imposes of a uniform distribution on a search space in the absence of all prior knowledge about the search target or the search space structure. The assumption is conserved under mapping. If the probability of finding a target in a search space is p, then the problem of finding the target in any subset of the search space is p. More generally, all some-to-many mappings of a uniform search space result in a new search space where the chance of doing better than p is 50-50. Consequently the chance of doing worse is 50-50. This result can be viewed as a confirming property of COI. To properly assess the significance of the COI for search, one must completely identify the precise sources of information that affect search performance. This discussion leads to resolution of the seeming conflict between COI and the observation that some search algorithms perform well on a large class of problems.

Grover's algorithm provides a quadratic speed-up over classical algorithms for unstructured database or library searches. This paper examines the robustness of Grover's search algorithm to a random phase error in the oracle and analyzes the complexity of the search process as a function of the scaling of the oracle error with database or library size. Both the discrete- and continuous-time implementations of the search algorithm are investigated. It is shown that unless the oracle phase error scales as O(N^(-1/4)), neither the discrete- nor the continuous-time implementation of Grover's algorithm is scalably robust to this error in the absence of error correction.

Most engineering optimization algorithms are based on numerical linear and nonlinear programming methods that require substantial gradient information and usually seek to improve the solution in the neighborhood of a starting point. These algorithms, however, reveal a limited approach to complicated real-world optimization problems. If there is more than one local optimum in the problem, the result may depend on the selection of an initial point, and the obtained optimal solution may not necessarily be the global optimum. This paper describes a new harmony search (HS) meta-heuristic algorithm-based approach for engineering optimization problems with continuous design variables. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. Various engineering optimization problems, including mathematical function minimization and structural engineering optimization problems, are presented to demonstrate the effectiveness and robustness of the HS algorithm. The results indicate that the proposed approach is a powerful search and optimization technique that may yield better solutions to engineering problems than those obtained using current algorithms. required) when solving difficult and complex engineering optimization problems. These algorithms include simulated annealing, tabu search, and evolutionary computation methods.

This paper shows a way to speed up search by using an encoding at bit level to model a particular domain. A bitboard is an unsigned integer whose bits have been given an interpretation in the world of discourse. In models encoded in such a way, states will be described internally as a set of bitboard tuples, whilst operators which allow for transitions between states are essentially bitwise operations over elements belonging to that set.

In this paper the recently developed "Floating Search" algorithms are presented and modified to a more compact form facilitating their direct comparison with the well known (11 r ) search. The properties ofthe Floating Search methods are investigated, especially with respect to their tolerance t o nonmonotonic criteria. Their computational efficiency is demonstrated b y results on real data of high dimensionality.

Trappy minimax is a game-independent extension of the minimax adversarial search algorithm that attempts to take advantage of human frailty. Whereas minimax assumes best play by the opponent, trappy minimax tries to predict when an opponent might make a mistake by comparing the various scores returned through iterativedeepening. Sometimes it chooses a slightly inferior move, if there is an indication that the opponent may fall into a trap, and if the potential profit is high. The algorithm was implemented in an Othello program named Desdemona, and tested against both computer and human opponents. Desdemona achieved a higher rating against human opposition on Yahoo! Games when using the trappy algorithm than when it used standard minimax.

To ensure the quality of the nanoimprint fabricated optical gratings, optical scatterometry (OS) is an efficient and effective mean to diagnose the actual fabricated geometry. To facilitate the diagnosis process, efficient pattern matching algorithms over a huge database are of great importance.

In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm based on the law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions.

Nature is the principal source for proposing new optimization methods such as genetic algorithms (GA) and simulated annealing (SA) methods. All traditional evolutionary algorithms are heuristic population-based search procedures that incorporate random variation and selection. The main contribution of this study is that it proposes a novel optimization method that relies on one of the theories of the evolution of the universe; namely, the Big Bang and Big Crunch Theory. In the Big Bang phase, energy dissipation produces disorder and randomness is the main feature of this phase; whereas, in the Big Crunch phase, randomly distributed particles are drawn into an order. Inspired by this theory, an optimization algorithm is constructed, which will be called the Big Bang-Big Crunch (BB-BC) method that generates random points in the Big Bang phase and shrinks those points to a single representative point via a center of mass or minimal cost approach in the Big Crunch phase. It is shown that the performance of the new (BB-BC) method demonstrates superiority over an improved and enhanced genetic search algorithm also developed by the authors of this study, and outperforms the classical genetic algorithm (GA) for many benchmark test functions. q

One approach for utilizing geoscience models for management or policy analysis is via a simulationbased optimization framework-where an underlying model is linked with an optimization search algorithm. In this regard, MATLAB and Python are high-level programming languages that implement numerous optimization routines, including gradient-based, heuristic, and direct-search optimizers. The ever-expanding number of available algorithms makes it challenging for practitioners to identify optimizers that deliver good performance when applied to problems of interest. Thus, the primary contribution of this paper is to present a series of numerical experiments that investigated the performance of various MATLAB and Python optimizers. The experiments considered two simulationbased optimization case studies involving groundwater flow and contaminant transport. One case study examined the design of a pump-and-treat system for groundwater remediation, while the other considered least-squares calibration of a model of strontium (Sr) transport. Using these case studies, the performance of 12 different MATLAB and Python optimizers was compared. Overall, the Hooke-Jeeves direct search algorithm yielded the best performance in terms of identifying least-cost and best-fit solutions to the design and calibration problems, respectively. The IFFCO (implicit filtering for constrained optimization) direct search algorithm and the dynamically dimensioned search (DDS) heuristic algorithm also consistently yielded good performance and were up to 80% more efficient than Hooke-Jeeves when applied to the pump-and-treat problem. These results provide empirical evidence that, relative to gradient-and population-based alternatives, direct search algorithms and heuristic variants, such as DDS, are good choices for application to simulation-based optimization problems involving groundwater management.