Hill-climbing Search Research Articles

Directed Plateau Search for MAX-k-SAT

Local search algorithms for MAX-k-SAT must often explore large regions of mutually connected equal moves, or plateaus, typically by taking random walks through the region. In this paper, we develop a surrogate plateau "gradient" function using a Walsh transform of the objective function. This function gives the mean value of the objective function over localized volumes of the search space. This information can be used to direct search through plateaus more quickly. The focus of this paper is on demonstrating that formal analysis of search space structure can direct existing algorithms in a more principled manner than random walks. We show that embedding the gradient computation into a hill-climbing local search for MAX-k-SAT improves its convergence profile.

Intelligent approach to maximum power point tracking control strategy for variable-speed wind turbine generation system

Abstract To achieve maximum power point tracking (MPPT) for wind power generation systems, the rotational speed of wind turbines should be adjusted in real time according to wind speed. In this paper, a Wilcoxon radial basis function network (WRBFN) with hill-climb searching (HCS) MPPT strategy is proposed for a permanent magnet synchronous generator (PMSG) with a variable-speed wind turbine. A high-performance online training WRBFN using a back-propagation learning algorithm with modified particle swarm optimization (MPSO) regulating controller is designed for a PMSG. The MPSO is adopted in this study to adapt to the learning rates in the back-propagation process of the WRBFN to improve the learning capability. The MPPT strategy locates the system operation points along the maximum power curves based on the dc-link voltage of the inverter, thus avoiding the generator speed detection.

DISCRETE OPTIMIZATION ALGORITHMS IN REAL-TIME VISUAL TRACKING

In this work we introduce a novel formulation of the association problem in visual tracking systems as a discrete optimization problem. The full data association problem is formulated as a search for the best tracking configuration to match hypothesis. We have implemented three local search algorithms: Hill Climbing, Simulated Annealing, and Tabu Search algorithms. These algorithms are guided by heuristic evaluation function which takes into account structural and specific information such as distance and shape. We have also introduced a novel technique in order to achieve incrementality in discrete optimization algorithms searching on an indirect space. We will show how this incrementality produces efficient and fast results, especially so when real-time is a hard constraint. The results obtained with the three discrete optimization algorithms are compared with other well-known and powerful computer vision tracking algorithms. We will prove the effectiveness and robustness of the discrete optimization algorithms in five different real-life scenarios.

Development of a case-based reasoning cognitive engine for IEEE 802.22 WRAN applications

On Nov. 4 2008, the Federal Communications Commission adopted rules for unlicensed use of television white spaces. The IEEE 802.22 Wireless Regional Area Networks (WRAN) standard is the first IEEE standard utilizing cognitive radio (CR) technology to exploit the television white space. A decision engine that is able to respond to the changes in the radio environment is necessary to efficiently exploit underutilized spectrum resources and avoid interfering with the licensed systems (e.g., TV services). This paper discusses the development of a case-based reasoning cognitive engine (CBR-CE) for the IEEE 802.22 WRAN applications. The performance of the CBR-CE is evaluated under various radio scenarios and compared to that of several multi objective search based algorithms, including the hill climbing search (HCS) and the genetic algorithm (GA). The simulation results show that the developed CBR-CE can achieve comparable utility with faster adaptation than the search based cognitive engines after appropriate training / learning. The learning process of the CBR is also simulated and discussed.

Species Tree Inference by Minimizing Deep Coalescences

In a 1997 seminal paper, W. Maddison proposed minimizing deep coalescences, or MDC, as an optimization criterion for inferring the species tree from a set of incongruent gene trees, assuming the incongruence is exclusively due to lineage sorting. In a subsequent paper, Maddison and Knowles provided and implemented a search heuristic for optimizing the MDC criterion, given a set of gene trees. However, the heuristic is not guaranteed to compute optimal solutions, and its hill-climbing search makes it slow in practice. In this paper, we provide two exact solutions to the problem of inferring the species tree from a set of gene trees under the MDC criterion. In other words, our solutions are guaranteed to find the tree that minimizes the total number of deep coalescences from a set of gene trees. One solution is based on a novel integer linear programming (ILP) formulation, and another is based on a simple dynamic programming (DP) approach. Powerful ILP solvers, such as CPLEX, make the first solution appealing, particularly for very large-scale instances of the problem, whereas the DP-based solution eliminates dependence on proprietary tools, and its simplicity makes it easy to integrate with other genomic events that may cause gene tree incongruence. Using the exact solutions, we analyze a data set of 106 loci from eight yeast species, a data set of 268 loci from eight Apicomplexan species, and several simulated data sets. We show that the MDC criterion provides very accurate estimates of the species tree topologies, and that our solutions are very fast, thus allowing for the accurate analysis of genome-scale data sets. Further, the efficiency of the solutions allow for quick exploration of sub-optimal solutions, which is important for a parsimony-based criterion such as MDC, as we show. We show that searching for the species tree in the compatibility graph of the clusters induced by the gene trees may be sufficient in practice, a finding that helps ameliorate the computational requirements of optimization solutions. Further, we study the statistical consistency and convergence rate of the MDC criterion, as well as its optimality in inferring the species tree. Finally, we show how our solutions can be used to identify potential horizontal gene transfer events that may have caused some of the incongruence in the data, thus augmenting Maddison's original framework. We have implemented our solutions in the PhyloNet software package, which is freely available at: http://bioinfo.cs.rice.edu/phylonet.

Reactive Tabu Search in a Team-Learning Problem

In designing an MBA core curriculum, student cohorts are assigned to a number of sections in each term. In a given term, different sections have exclusive schedules, while the cohorts in the same section have an identical schedule. By altering the cohorts in the sections through the terms, any cohort can meet as many other cohorts as possible via the core courses. We model this problem as a quadratic integer programming problem and construct the performance measure to characterize the properties of the problem. We design a reactive tabu search metaheuristic that solves the model effectively. Two other solution methods, constraint logic programming and an improved hill-climbing search heuristic, are also discussed. We analyze the computational results for the cases in practice.

A New Crossover Operator in Genetic Programming for Object Classification

The crossover operator has been considered "the centre of the storm" in genetic programming (GP). However, many existing GP approaches to object recognition suggest that the standard GP crossover is not sufficiently powerful in producing good child programs due to the totally random choice of the crossover points. To deal with this problem, this paper introduces an approach with a new crossover operator in GP for object recognition, particularly object classification. In this approach, a local hill-climbing search is used in constructing good building blocks, a weight called looseness is introduced to identify the good building blocks in individual programs, and the looseness values are used as heuristics in choosing appropriate crossover points to preserve good building blocks. This approach is examined and compared with the standard crossover operator and the headless chicken crossover (HCC) method on a sequence of object classification problems. The results suggest that this approach outperforms the HCC, the standard crossover, and the standard crossover operator with hill climbing on all of these problems in terms of the classification accuracy. Although this approach spends a bit longer time than the standard crossover operator, it significantly improves the system efficiency over the HCC method.

A hybrid genetic algorithm for a class of global optimization problems with box constraints

In this paper, a new hybrid genetic algorithm is proposed, which combines the genetic algorithm with hill-climbing search steps differently from some former algorithms. The new algorithm can be widely applied to a class of global optimization problems for continuous functions with box constraints. Finally, numerical examples show that this algorithm can yield the global optimum with high efficiency.

Optimization using finite element analysis, neural network, and experiment in tube hydroforming of aluminium T joints

In tube hydroforming (THF) of T joints, loading conditions (internal pressure and axial feeding) should be determined in such a way that the tube does not wrinkle or burst and is fully calibrated. In the current study THF of an aluminium T joint is simulated with the finite element method (FEM) using a commercial code. An explicit method is used to overcome convergence problems that are encountered in an implicit method. Internal pressure and axial feeding are two variables in the optimization problem and the loading path is optimized. The objective function is the clamping force, and the constraints of wrinkling, minimum thickness, and calibration should be achieved. The objective and constraint functions are obtained by training a neural network and the objective function is minimized using several optimization methods including hill-climbing search, simulated annealing, and complex method. The axial feeding and internal pressure obtained by optimization methods are used to conduct an experiment. Thickness distribution, calibration pressure, and axial feeding in experiment and FEM are compared and it is shown that there is a good agreement between them.

Evolving a Bayesian classifier for ECG-based age classification in medical applications

Objective To classify patients by age based upon information extracted from their electrocardiograms (ECGs). To develop and compare the performance of Bayesian classifiers. Methods and material We present a methodology for classifying patients according to statistical features extracted from their ECG signals using a genetically evolved Bayesian network classifier. Continuous signal feature variables are converted to a discrete symbolic form by thresholding, to lower the dimensionality of the signal. This simplifies calculation of conditional probability tables for the classifier, and makes the tables smaller. Two methods of network discovery from data were developed and compared: the first using a greedy hill-climb search and the second employed evolutionary computing using a genetic algorithm (GA). Results and conclusions The evolved Bayesian network performed better (86.25% AUC) than both the one developed using the greedy algorithm (65% AUC) and the naïve Bayesian classifier (84.75% AUC). The methodology for evolving the Bayesian classifier can be used to evolve Bayesian networks in general thereby identifying the dependencies among the variables of interest. Those dependencies are assumed to be non-existent by naïve Bayesian classifiers. Such a classifier can then be used for medical applications for diagnosis and prediction purposes.

A guided simulated annealing search for solving the pick-up and delivery problem with time windows and capacity constraints

Routing and scheduling requests with pick-ups and deliveries is still one of the greatest operative challenges in inter-company logistics. Pick-up and delivery activities have to be bundled into efficient routes and their sequence has to be optimised within the routes without violating time and capacity constraints. The objective is to find a schedule of routes with minimal total costs. These costs result from the arising travelling costs, costs due to waiting and service times, and due to dispatching vehicles. This paper presents a guided local search method based on simulated annealing for solving this kind of routing and scheduling problem. Additionally, a new more realistic objective function that covers the total decision-relevant costs is introduced. The computational results show that the algorithm presented clearly outperforms standard implementations of simulated annealing and hill climber search.

A DEEP IMPROVER OF TWO-MOVE CHESS MATE PROBLEMS

Computer composition of high-quality chess mate problems is relatively an uninvestigated research domain. A previous model, called an Improver of Chess Problems, which is based on a hill-climbing search, improved slightly the quality of 10 out of 36 known problems (about 28%). In this article, we describe an improved model, called Deep Improver of Chess Problems. This model uses an improved version of Bounded Depth First Search. The experiment, we carry out on the same problems, show that the quality of 32 problems (about 89%) has been improved. Moreover, some of the improvements are meaningful and impressive.

Approximation algorithm for periodic real-time tasks with workload-dependent running-time functions

This paper addresses the problem of resource allocation for distributed real-time periodic tasks, operating in environments that undergo unpredictable changes and that defy the specification of meaningful worst-case execution times. These tasks are supplied by input data originating from various environmental workload sources. Rather than using worst-case execution times (WCETs) to describe the CPU usage of the tasks, we assume here that execution profiles are given to describe the running time of the tasks in terms of the size of the input data of each workload source. The objective of resource allocation is to produce an initial allocation that is robust against fluctuations in the environmental parameters. We try to maximize the input size (workload) that can be handled by the system, and hence to delay possible (costly) reallocations as long as possible. We present an approximation algorithm based on first-fit and binary search that we call FFBS. As we show here, the first-fit algorithm produces solutions that are often close to optimal. In particular, we show analytically that FFBS is guaranteed to produce a solution that is at least 41% of optimal, asymptotically, under certain reasonable restrictions on the running times of tasks in the system. Moreover, we show that if at most 12% of the system utilization is consumed by input independent tasks (e.g., constant time tasks), then FFBS is guaranteed to produce a solution that is at least 33% of optimal, asymptotically. Moreover, we present simulations to compare FFBS approximation algorithm with a set of standard (local search) heuristics such as hill-climbing, simulated annealing, and random search. The results suggest that FFBS, in combination with other local improvement strategies, may be a reasonable approach for resource allocation in dynamic real-time systems.

Representation and structural difficulty in genetic programming

Standard tree-based genetic programming suffers from a structural difficulty problem in that it is unable to search effectively for solutions requiring very full or very narrow trees. This deficiency has been variously explained as a consequence of restrictions imposed by the tree structure or as a result of the numerical distribution of tree shapes. We show that by using a different tree-based representation and local (insertion and deletion) structural modification operators, that this problem can be almost eliminated even with trivial (stochastic hill-climbing) search methods, thus eliminating the above explanations. We argue, instead, that structural difficulty is a consequence of the large step size of the operators in standard genetic programming, which is itself a consequence of the fixed-arity property embodied in its representation.

The max-min hill-climbing Bayesian network structure learning algorithm

We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. In our extensive empirical evaluation MMHC outperforms on average and in terms of various metrics several prototypical and state-of-the-art algorithms, namely the PC, Sparse Candidate, Three Phase Dependency Analysis, Optimal Reinsertion, Greedy Equivalence Search, and Greedy Search. These are the first empirical results simultaneously comparing most of the major Bayesian network algorithms against each other. MMHC offers certain theoretical advantages, specifically over the Sparse Candidate algorithm, corroborated by our experiments. MMHC and detailed results of our study are publicly available at http://www.dsl-lab.org/supplements/mmhc_paper/mmhc_index.html.

An Influence of Nonlinearities to Storage Capacity of Neural Networks

The more realistic neural soma and synaptic nonlinear relations and an alternative mean field theory (MFT) approach relevant for strongly interconnected systems as a cortical matter are considered. The general procedure of averaging the quenched random states in the fully-connected networks for MFT, as usually, is based on the Boltzmann Machine learning. But this approach requires an unrealistically large number of samples to provide a reliable performance. We suppose an alternative MFT with deterministic features instead of stochastic nature of searching a solution a set of large number equations. Of course, this alternative theory will not be strictly valid for infinite number of elements. Another property of generalization is an inclusion of the additional member in the effective Hamiltonian allowing to improve the stochastic hill-climbing search of the solution not dropping into local minima of the energy function. Especially, we pay attention to increasing of neural networks retrieval capability transforming the replica-symmetry model by including of different nonlinear elements. Some results of numerical modeling as well as the wide discussion of neural systems storage capacity are presented.

Molecular Systematics and Biogeography of the Southern South American Freshwater “Crabs” Aegla (Decapoda: Anomura: Aeglidae) Using Multiple Heuristic Tree Search Approaches

Recently new heuristic genetic algorithms such as Treefinder and MetaGA have been developed to search for optimal trees in a maximum likelihood (ML) framework. In this study we combined these methods with other standard heuristic approaches such as ML and maximum parsimony hill-climbing searches and Bayesian inference coupled with Markov chain Monte Carlo techniques under homogeneous and mixed models of evolution to conduct an extensive phylogenetic analysis of the most abundant and widely distributed southern South American freshwater"crab,"the Aegla(Anomura: Aeglidae). A total of 167 samples representing 64 Aegla species and subspecies were sequenced for one nuclear (28S rDNA) and four mitochondrial (12S and 16S rDNA, COI, and COII) genes (5352 bp total). Additionally, six other anomuran species from the genera Munida,Pachycheles, and Uroptychus(Galatheoidea), Lithodes(Paguroidea), and Lomis(Lomisoidea) and the nuclear 18S rDNA gene (1964 bp) were included in preliminary analyses for rooting the Aegla tree. Nonsignificantly different phylogenetic hypotheses resulted from all the different heuristic methods used here, although the best scored topologies found under the ML hill-climbing, Bayesian, and MetaGA approaches showed considerably better likelihood scores (Delta> 54) than those found under the MP and Treefinder approaches. Our trees provided strong support for most of the recognized Aegla species except for A. cholchol,A. jarai,A. parana,A. marginata, A. platensis, and A. franciscana, which may actually represent multiple species. Geographically, the Aegla group was divided into a basal western clade (21 species and subspecies) composed of two subclades with overlapping distributions, and a more recent central-eastern clade (43 species) composed of three subclades with fairly well-recognized distributions. This result supports the Pacific-Origin Hypothesis postulated for the group; alternative hypotheses of Atlantic or multiple origins were significantly rejected by our analyses. Finally, we combined our phylogenetic results with previous hypotheses of South American paleodrainages since the Jurassic to propose a biogeographical framework of the Aegla radiation.

Metropolitan pollution reduction by intelligent negotiation

This paper discusses the issue of pollution reduction in metropolises by means of intelligent negotiation in multi-agent systems. For situations of complete information, it gives a stochastic hill-climbing search algorithm for computing the pollution-reduction solutions; For situations of incomplete information, it puts forward a genetic algorithm for computing the best solutions for every plants subjectively and proposes market-mechanism-based algorithm for computing the emission-redistribution solutions objectively.

Operational Optimization of Water Distribution Systems Using a Hybrid Genetic Algorithm

Genetic algorithm (GA) optimization is well suited for optimizing the operation of water distribution systems, especially large and complex systems. GAs have good initial convergence characteristics, but slow down considerably once the region of optimal solution has been identified. In this study the efficiency of GA operational optimization was improved through a hybrid method which combines the GA method with a hillclimber search strategy. Hillclimber strategies complement GAs by being efficient in finding a local optimum. Two hillclimber strategies, the Hooke and Jeeves and Fibonacci methods, were investigated. The hybrid method proved to be superior to the pure GA in finding a good solution quickly, both when applied to a test problem and to a large existing water distribution system.

Gate Location Optimization in Injection Molding By Using Modified Hill-Climbing Algorithm

In injection mold, the part quality and the proper working of the mold is greatly affected by the gate location, because it influences the manner in which the plastic flows into the mold cavity. Therefore, the product quality can be greatly improved by determining the optimum gate location. In this article, we develop a general methodology that automatically predicts the “optimal” gate location of injection molds based on injection-molding simulation. First, we defined cost function, which measure the performances of polymer injection operation affected by the gate location. This function includes four items, such as filling pressure, filling time difference between different flow paths, temperature difference, and overpack percentage, which are the function of gate location. Next, a modified hill-climbing algorithm that combines the designer's intuition with a deterministic hill-climbing search was used to search the optimal gate location. The model analyzed demonstrates that the proposed method is viable and robust.