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Showing papers on "Heuristic published in 2013"


Journal ArticleDOI
TL;DR: A critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas are presented.
Abstract: Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.

1,023 citations


Journal ArticleDOI
TL;DR: The experimental results show that the proposed black hole algorithm outperforms other traditional heuristic algorithms for several benchmark datasets.

963 citations


Journal ArticleDOI
TL;DR: An emerging area of mixed-integer optimal control that adds systems of ordinary differential equations to MINLP is described and a range of approaches for tackling this challenging class of problems are discussed, including piecewise linear approximations, generic strategies for obtaining convex relaxations for non-convex functions, spatial branch-and-bound methods, and a small sample of techniques that exploit particular types of non- Convex structures to obtain improved convex Relaxations.
Abstract: Many optimal decision problems in scientific, engineering, and public sector applications involve both discrete decisions and nonlinear system dynamics that affect the quality of the final design or plan. These decision problems lead to mixed-integer nonlinear programming (MINLP) problems that combine the combinatorial difficulty of optimizing over discrete variable sets with the challenges of handling nonlinear functions. We review models and applications of MINLP, and survey the state of the art in methods for solving this challenging class of problems.Most solution methods for MINLP apply some form of tree search. We distinguish two broad classes of methods: single-tree and multitree methods. We discuss these two classes of methods first in the case where the underlying problem functions are convex. Classical single-tree methods include nonlinear branch-and-bound and branch-and-cut methods, while classical multitree methods include outer approximation and Benders decomposition. The most efficient class of methods for convex MINLP are hybrid methods that combine the strengths of both classes of classical techniques.Non-convex MINLPs pose additional challenges, because they contain non-convex functions in the objective function or the constraints; hence even when the integer variables are relaxed to be continuous, the feasible region is generally non-convex, resulting in many local minima. We discuss a range of approaches for tackling this challenging class of problems, including piecewise linear approximations, generic strategies for obtaining convex relaxations for non-convex functions, spatial branch-and-bound methods, and a small sample of techniques that exploit particular types of non-convex structures to obtain improved convex relaxations.We finish our survey with a brief discussion of three important aspects of MINLP. First, we review heuristic techniques that can obtain good feasible solution in situations where the search-tree has grown too large or we require real-time solutions. Second, we describe an emerging area of mixed-integer optimal control that adds systems of ordinary differential equations to MINLP. Third, we survey the state of the art in software for MINLP.

611 citations


Journal ArticleDOI
TL;DR: This work investigates variants of Lloyd's heuristic for clustering high dimensional data in an attempt to explain its popularity (a half century after its introduction) among practitioners, and proposes and justifies a clusterability criterion for data sets.
Abstract: We investigate variants of Lloyd's heuristic for clustering high-dimensional data in an attempt to explain its popularity (a half century after its introduction) among practitioners, and in order to suggest improvements in its application. We propose and justify a clusterability criterion for data sets. We present variants of Lloyd's heuristic that quickly lead to provably near-optimal clustering solutions when applied to well-clusterable instances. This is the first performance guarantee for a variant of Lloyd's heuristic. The provision of a guarantee on output quality does not come at the expense of speed: some of our algorithms are candidates for being faster in practice than currently used variants of Lloyd's method. In addition, our other algorithms are faster on well-clusterable instances than recently proposed approximation algorithms, while maintaining similar guarantees on clustering quality. Our main algorithmic contribution is a novel probabilistic seeding process for the starting configuration of a Lloyd-type iteration.

398 citations


Posted Content
TL;DR: The Fast Marching Tree algorithm (FMT*) as mentioned in this paper is a sampling-based motion planning algorithm for high-dimensional configuration spaces that is proven to be asymptotically optimal and converges to an optimal solution faster than its state-of-the-art counterparts.
Abstract: In this paper we present a novel probabilistic sampling-based motion planning algorithm called the Fast Marching Tree algorithm (FMT*). The algorithm is specifically aimed at solving complex motion planning problems in high-dimensional configuration spaces. This algorithm is proven to be asymptotically optimal and is shown to converge to an optimal solution faster than its state-of-the-art counterparts, chiefly PRM* and RRT*. The FMT* algorithm performs a "lazy" dynamic programming recursion on a predetermined number of probabilistically-drawn samples to grow a tree of paths, which moves steadily outward in cost-to-arrive space. As a departure from previous analysis approaches that are based on the notion of almost sure convergence, the FMT* algorithm is analyzed under the notion of convergence in probability: the extra mathematical flexibility of this approach allows for convergence rate bounds--the first in the field of optimal sampling-based motion planning. Specifically, for a certain selection of tuning parameters and configuration spaces, we obtain a convergence rate bound of order $O(n^{-1/d+\rho})$, where $n$ is the number of sampled points, $d$ is the dimension of the configuration space, and $\rho$ is an arbitrarily small constant. We go on to demonstrate asymptotic optimality for a number of variations on FMT*, namely when the configuration space is sampled non-uniformly, when the cost is not arc length, and when connections are made based on the number of nearest neighbors instead of a fixed connection radius. Numerical experiments over a range of dimensions and obstacle configurations confirm our theoretical and heuristic arguments by showing that FMT*, for a given execution time, returns substantially better solutions than either PRM* or RRT*, especially in high-dimensional configuration spaces and in scenarios where collision-checking is expensive.

254 citations


Proceedings ArticleDOI
28 May 2013
TL;DR: This paper discusses the state of the art heuristic malware detection methods and briefly overview various features used in these methods such as API Calls, OpCodes, N-Grams etc and discuss their advantages and disadvantages.
Abstract: Malware is a malicious code which is developed to harm a computer or network. The number of malwares is growing so fast and this amount of growth makes the computer security researchers invent new methods to protect computers and networks. There are three main methods used to malware detection: Signature based, Behavioral based and Heuristic ones. Signature based malware detection is the most common method used by commercial antiviruses but it can be used in the cases which are completely known and documented. Behavioral malware detection was introduced to cover deficiencies of signature based method. However, because of some shortcomings, the heuristic methods have been introduced. In this paper, we discuss the state of the art heuristic malware detection methods and briefly overview various features used in these methods such as API Calls, OpCodes, N-Grams etc. and discuss their advantages and disadvantages.

221 citations


Posted Content
TL;DR: In this article, the problem of finding a small set of variables to affect with an input so that the resulting system is controllable is shown to be NP-hard, and it is shown that even approximating the minimum number of variables that need to be affected within a multiplicative factor of $c \log n$ is NP hard for some positive $c.
Abstract: Given a linear system, we consider the problem of finding a small set of variables to affect with an input so that the resulting system is controllable. We show that this problem is NP-hard; indeed, we show that even approximating the minimum number of variables that need to be affected within a multiplicative factor of $c \log n$ is NP-hard for some positive $c$. On the positive side, we show it is possible to find sets of variables matching this inapproximability barrier in polynomial time. This can be done by a simple greedy heuristic which sequentially picks variables to maximize the rank increase of the controllability matrix. Experiments on Erdos-Renyi random graphs demonstrate this heuristic almost always succeeds at findings the minimum number of variables.

214 citations


Journal ArticleDOI
TL;DR: A new definition of attribute reduct for decision-theoretic rough set models is provided and a heuristic approach, a genetic approach and a simulated annealing approach to the new problem are proposed.

210 citations


Journal ArticleDOI
TL;DR: In this paper, a decision maker is responsible to dynamically collect observations so as to enhance his information about an underlying phenomena of interest in a speedy manner while accounting for the penalty of wrong declaration.
Abstract: Consider a decision maker who is responsible to dynamically collect observations so as to enhance his information about an underlying phenomena of interest in a speedy manner while accounting for the penalty of wrong declaration. Due to the sequential nature of the problem, the decision maker relies on his current information state to adaptively select the most “informative” sensing action among the available ones. In this paper, using results in dynamic programming, lower bounds for the optimal total cost are established. The lower bounds characterize the fundamental limits on the maximum achievable information acquisition rate and the optimal reliability. Moreover, upper bounds are obtained via an analysis of two heuristic policies for dynamic selection of actions. It is shown that the first proposed heuristic achieves asymptotic optimality, where the notion of asymptotic optimality, due to Chernoff, implies that the relative difference between the total cost achieved by the proposed policy and the optimal total cost approaches zero as the penalty of wrong declaration (hence the number of collected samples) increases. The second heuristic is shown to achieve asymptotic optimality only in a limited setting such as the problem of a noisy dynamic search. However, by considering the dependency on the number of hypotheses, under a technical condition, this second heuristic is shown to achieve a nonzero information acquisition rate, establishing a lower bound for the maximum achievable rate and error exponent. In the case of a noisy dynamic search with size-independent noise, the obtained nonzero rate and error exponent are shown to be maximum.

181 citations


Journal ArticleDOI
01 Apr 2013
TL;DR: The proposed approach combines in the most effective way the properties of two of the most popular evolutionary optimization techniques now in use for power system optimization, the Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms.
Abstract: This paper presents the design and application of an efficient hybrid heuristic search method to solve the practical economic dispatch problem considering many nonlinear characteristics of power generators, and their operational constraints, such as transmission losses, valve-point effects, multi-fuel options, prohibited operating zones, ramp rate limits and spinning reserve. These practical operation constraints which can usually be found at the same time in realistic power system operations make the economic load dispatch problem a nonsmooth optimization problem having complex and nonconvex features with heavy equality and inequality constraints. The proposed approach combines in the most effective way the properties of two of the most popular evolutionary optimization techniques now in use for power system optimization, the Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. To improve the global optimization property of DE, the PSO procedure is integrated as additional mutation operator. The effectiveness of the proposed algorithm (termed DEPSO) is demonstrated by solving four kinds of ELD problems with nonsmooth and nonconvex solution spaces. The comparative results with some of the most recently published methods confirm the effectiveness of the proposed strategy to find accurate and feasible optimal solutions for practical ELD problems.

176 citations


Proceedings ArticleDOI
06 May 2013
TL;DR: In this article, the problem of optimal multi-robot path planning (MPP) on graphs was studied and two ILP models were proposed to compute minimum last arrival time and minimum total distance solutions for the MPP problem.
Abstract: In this paper, we study the problem of optimal multi-robot path planning (MPP) on graphs. We propose two multiflow based integer linear programming (ILP) models that compute minimum last arrival time and minimum total distance solutions for our MPP formulation, respectively. The resulting algorithms from these ILP models are complete and guaranteed to yield true optimal solutions. In addition, our flexible framework can easily accommodate other variants of the MPP problem. Focusing on the time optimal algorithm, we evaluate its performance, both as a stand alone algorithm and as a generic heuristic for quickly solving large problem instances. Computational results confirm the effectiveness of our method.

Journal ArticleDOI
01 Apr 2013
TL;DR: This work presents a cost-efficient task-scheduling algorithm using two heuristic strategies that dynamically maps tasks to the most cost- efficient VMs based on the concept of Pareto dominance and reduces the monetary costs of non-critical tasks.
Abstract: Executing a large program using clouds is a promising approach, as this class of programs may be decomposed into multiple sequences of tasks that can be executed on multiple virtual machines (VMs) in a cloud. Such sequences of tasks can be represented as a directed acyclic graph (DAG), where nodes are tasks and edges are precedence constraints between tasks. Cloud users pay for what their programs actually use according to the pricing models of the cloud providers. Early task scheduling algorithms are focused on minimizing makespan, without mechanisms to reduce the monetary cost incurred in the setting of clouds. We present a cost-efficient task-scheduling algorithm using two heuristic strategies.The first strategy dynamically maps tasks to the most cost-efficient VMs based on the concept of Pareto dominance. The second strategy, a complement to the first strategy, reduces the monetary costs of non-critical tasks. We carry out extensive numerical experiments on large DAGs generated at random as well as on real applications. The simulation results show that our algorithm can substantially reduce monetary costs while producing makespan as good as the best known task-scheduling algorithm can provide.

Patent
04 Sep 2013
TL;DR: In this article, a botnet detection approach based on a heuristic analysis of the suspicious network traffic behavior using a processor is presented, which includes command and control traffic associated with a bot master.
Abstract: In some embodiments, heuristic botnet detection is provided. In some embodiments, heuristic botnet detection includes monitoring network traffic to identify suspicious network traffic; and detecting a bot based on a heuristic analysis of the suspicious network traffic behavior using a processor, in which the suspicious network traffic behavior includes command and control traffic associated with a bot master. In some embodiments, heuristic botnet detection further includes assigning a score to the monitored network traffic, in which the score corresponds to a botnet risk characterization of the monitored network traffic (e.g., based on one or more heuristic botnet detection techniques); increasing the score based on a correlation of additional suspicious behaviors associated with the monitored network traffic (e.g., based on one or more heuristic botnet detection techniques); and determining the suspicious behavior is associated with a botnet based on the score.

Journal ArticleDOI
TL;DR: A flexible modeling framework for IRP, which can accommodate various practical features and a simple algorithmic framework of an optimization based heuristic method is also proposed.

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a seed selection algorithm for the budgeted influence maximization problem with an approximation ratio of (1-1/√e) for a fixed budget and an arbitrary cost for selecting each node.
Abstract: Given a fixed budget and an arbitrary cost for selecting each node, the budgeted influence maximization (BIM) problem concerns selecting a set of seed nodes to disseminate some information that maximizes the total number of nodes influenced (termed as influence spread) in social networks at a total cost no more than the budget. Our proposed seed selection algorithm for the BIM problem guarantees an approximation ratio of (1-1/√e). The seed selection algorithm needs to calculate the influence spread of candidate seed sets, which is known to be #P-complex. Identifying the linkage between the computation of marginal probabilities in Bayesian networks and the influence spread, we devise efficient heuristic algorithms for the latter problem. Experiments using both large-scale social networks and synthetically generated networks demonstrate superior performance of the proposed algorithm with moderate computation costs. Moreover, synthetic datasets allow us to vary the network parameters and gain important insights on the impact of graph structures on the performance of different algorithms.

Proceedings ArticleDOI
27 Oct 2013
TL;DR: In this article, a static greedy algorithm, named StaticGreedy, is proposed to strictly guarantee the submodularity of influence spread function during the seed selection process, which makes the computational expense dramatically reduced by two orders of magnitude without loss of accuracy.
Abstract: Influence maximization, defined as a problem of finding a set of seed nodes to trigger a maximized spread of influence, is crucial to viral marketing on social networks. For practical viral marketing on large scale social networks, it is required that influence maximization algorithms should have both guaranteed accuracy and high scalability. However, existing algorithms suffer a scalability-accuracy dilemma: conventional greedy algorithms guarantee the accuracy with expensive computation, while the scalable heuristic algorithms suffer from unstable accuracyIn this paper, we focus on solving this scalability-accuracy dilemma. We point out that the essential reason of the dilemma is the surprising fact that the submodularity, a key requirement of the objective function for a greedy algorithm to approximate the optimum, is not guaranteed in all conventional greedy algorithms in the literature of influence maximization. Therefore a greedy algorithm has to afford a huge number of Monte Carlo simulations to reduce the pain caused by unguaranteed submodularity. Motivated by this critical finding, we propose a static greedy algorithm, named StaticGreedy, to strictly guarantee the submodularity of influence spread function during the seed selection process. The proposed algorithm makes the computational expense dramatically reduced by two orders of magnitude without loss of accuracy. Moreover, we propose a dynamical update strategy which can speed up the StaticGreedy algorithm by 2-7 times on large scale social networks.

Journal ArticleDOI
TL;DR: The computational results indicate that the three-stage algorithm is effective for finding high-quality solutions and can efficiently solve large problems.

Journal ArticleDOI
TL;DR: The Genetic Algorithm tool was able to overcome overfitting and improve validation fitness scores with acceptable computational costs and is flexible enough to embrace a variety of models as well as their specific fitness functions, thus offering a practical way to optimize the performance of land-use change models.
Abstract: Spatially explicit land-use models simulate the patterns of change on the landscape in response to coupled human-ecological dynamics. As these models become more complex involving larger than ever data sets, the need to improve calibration techniques as well as methods that test model accuracy also increases. To this end, we developed a Genetic Algorithm tool and applied it to optimize probability maps of deforestation generated from the Weights of Evidence method for 12 case-study sites in the Brazilian Amazon. We show that the Genetic Algorithm tool, after being constrained during the reproduction process within a specified range and trend of variation of the Weights of Evidence coefficients, was able to overcome overfitting and improve validation fitness scores with acceptable computational costs. In addition to modeling deforestation, the Genetic Algorithm tool coupled with the Weights of Evidence method is flexible enough to embrace a variety of models as well as their specific fitness functions, thus offering a practical way to optimize the performance of land-use change models.

Journal ArticleDOI
TL;DR: A new scenario reduction heuristic named forward selection in wait-and-see clusters (FSWC) is test in the context of long-term power generation expansion planning to mitigate the computational complexity of the widely-used forward selection heuristic for scenario reduction.

Journal ArticleDOI
TL;DR: A two-stage hyper-heuristic for the generation of a set of work centre-specific dispatching rules that achieve a significantly lower mean weighted tardiness than any of the benckmark rules is proposed.

Journal ArticleDOI
TL;DR: A review of the methods and algorithms developed to examine the area of construction schedule optimization (CSO) is undertaken and the developed algorithms can be classified into three methods: mathematical, heuristic and metaheuristic.
Abstract: Optimizing construction project scheduling has received a considerable amount of attention over the past 20 years. As a result, a plethora of methods and algorithms have been developed to address specific scenarios or problems. A review of the methods and algorithms that have been developed to examine the area of construction schedule optimization (CSO) is undertaken. The developed algorithms for solving the CSO problem can be classified into three methods: mathematical, heuristic and metaheuristic. The application of these methods to various scheduling problems is discussed and implications for future research are identified.

Journal ArticleDOI
TL;DR: A formal formulation, two heuristic algorithms are proposed, and a particle swarm optimization (PSO) algorithm is developed to effectively tackle a scheduling problem in a multiple-machine system where the computing speeds of the machines are allowed to be adjusted during the course of execution.

Journal ArticleDOI
TL;DR: Experimental results on low-rank structure learning demonstrate that the nonconvex heuristic methods, especially the log-sum heuristic recovery algorithm, generally perform much better than the convex-norm-based method (0 <; p <; 1) for both data with higher rank and with denser corruptions.
Abstract: In this paper, we propose a nonconvex framework to learn the essential low-rank structure from corrupted data. Different from traditional approaches, which directly utilizes convex norms to measure the sparseness, our method introduces more reasonable nonconvex measurements to enhance the sparsity in both the intrinsic low-rank structure and the sparse corruptions. We will, respectively, introduce how to combine the widely used lp norm (0 <; p <; 1) and log-sum term into the framework of low-rank structure learning. Although the proposed optimization is no longer convex, it still can be effectively solved by a majorization-minimization (MM)-type algorithm, with which the nonconvex objective function is iteratively replaced by its convex surrogate and the nonconvex problem finally falls into the general framework of reweighed approaches. We prove that the MM-type algorithm can converge to a stationary point after successive iterations. The proposed model is applied to solve two typical problems: robust principal component analysis and low-rank representation. Experimental results on low-rank structure learning demonstrate that our nonconvex heuristic methods, especially the log-sum heuristic recovery algorithm, generally perform much better than the convex-norm-based method (0 <; p <; 1) for both data with higher rank and with denser corruptions.

Journal ArticleDOI
TL;DR: This work designs and implements efficient parallel community detection heuristics; the first large-scale parallelization of the well-known Louvain method, as well as an extension of the method adding refinement; and an ensemble scheme combining the above.
Abstract: The amount of graph-structured data has recently experienced an enormous growth in many applications. To transform such data into useful information, fast analytics algorithms and software tools are necessary. One common graph analytics kernel is disjoint community detection (or graph clustering). Despite extensive research on heuristic solvers for this task, only few parallel codes exist, although parallelism will be necessary to scale to the data volume of real-world applications. We address the deficit in computing capability by a flexible and extensible community detection framework with shared-memory parallelism. Within this framework we design and implement efficient parallel community detection heuristics: A parallel label propagation scheme; the first large-scale parallelization of the well-known Louvain method, as well as an extension of the method adding refinement; and an ensemble scheme combining the above. In extensive experiments driven by the algorithm engineering paradigm, we identify the most successful parameters and combinations of these algorithms. We also compare our implementations with state-of-the-art competitors. The processing rate of our fastest algorithm often reaches 50M edges/second. We recommend the parallel Louvain method and our variant with refinement as both qualitatively strong and fast. Our methods are suitable for massive data sets with billions of edges.

Proceedings ArticleDOI
20 Jun 2013
TL;DR: This paper proves that a good experimental design is able to find a combination of algorithms that outperforms any of its composing algorithms by automatically selecting the most appropriate heuristic for each function and search phase and obtains statistically better results than the reference algorithm DECC-G.
Abstract: Continuous optimization is one of the most active research Iines in evolutionary and metaheuristic algorithms. Through CEC 2005 to CEC 2013 competitions, many different algorithms have been proposed to solve continuous problems. The advances on this type of problems are of capital importance as many real-world problems from very different domains (biology, engineering, data mining, etc.) can be formulated as the optimization of a continuous function. In this paper we describe the whole process of creating a competitive hybrid algorithm, from the experimental design to the final statistical validation of the resuIts. We prove that a good experimental design is able to find a combination of algorithms that outperforms any of its composing algorithms by automatically selecting the most appropriate heuristic for each function and search phase. We also show that the proposed algorithm obtains statistically better results than the reference algorithm DECC-G.

Journal ArticleDOI
TL;DR: A hierarchy of kinetic and macroscopic models is derived from a noisy variant of the heuristic behavioral Individual-Based Model of Ngai et al. where pedestrians are supposed to have constant speeds to seek the best compromise between navigation towards their target and collisions avoidance.
Abstract: We derive a hierarchy of kinetic and macroscopic models from a noisy variant of the heuristic behavioral Individual-Based Model of Ngai et al. (Disaster Med. Public Health Prep. 3:191–195, 2009) where pedestrians are supposed to have constant speeds. This IBM supposes that pedestrians seek the best compromise between navigation towards their target and collisions avoidance. We first propose a kinetic model for the probability distribution function of pedestrians. Then, we derive fluid models and propose three different closure relations. The first two closures assume that the velocity distribution function is either a Dirac delta or a von Mises-Fisher distribution respectively. The third closure results from a hydrodynamic limit associated to a Local Thermodynamical Equilibrium. We develop an analogy between this equilibrium and Nash equilibria in a game theoretic framework. In each case, we discuss the features of the models and their suitability for practical use.

Journal ArticleDOI
TL;DR: A recovery model is explored for a two-stage production and inventory system with the possibility of transportation disruption, showing that the optimal recovery schedule is highly dependent on the relationship between the backorder cost and the lost sales cost parameters.

Posted Content
TL;DR: In this paper, the authors consider the problem of uncertain edge costs, with potential probabilistic dependencies among the costs, and identify a weaker stochastic consistency condition that justifies a generalized dynamic-programming approach based on stochastically dominance.
Abstract: Standard algorithms for finding the shortest path in a graph require that the cost of a path be additive in edge costs, and typically assume that costs are deterministic. We consider the problem of uncertain edge costs, with potential probabilistic dependencies among the costs. Although these dependencies violate the standard dynamic-programming decomposition, we identify a weaker stochastic consistency condition that justifies a generalized dynamic-programming approach based on stochastic dominance. We present a revised path-planning algorithm and prove that it produces optimal paths under time-dependent uncertain costs. We test the algorithm by applying it to a model of stochastic bus networks, and present empirical performance results comparing it to some alternatives. Finally, we consider extensions of these concepts to a more general class of problems of heuristic search under uncertainty.

Posted Content
TL;DR: Experimental results suggest that for learning probabilities of belief networks smoothing is helpful, which justifies the use of search heuristics based on the Bayesian measure of Cooper and Herskovits and a minimum description length measure.
Abstract: Bayesian belief network learning algorithms have three basic components: a measure of a network structure and a database, a search heuristic that chooses network structures to be considered, and a method of estimating the probability tables from the database. This paper contributes to all these three topics. The behavior of the Bayesian measure of Cooper and Herskovits and a minimum description length (MDL) measure are compared with respect to their properties for both limiting size and finite size databases. It is shown that the MDL measure has more desirable properties than the Bayesian measure when a distribution is to be learned. It is shown that selecting belief networks with certain minimallity properties is NP-hard. This result justifies the use of search heuristics instead of exact algorithms for choosing network structures to be considered. In some cases, a collection of belief networks can be represented by a single belief network which leads to a new kind of probability table estimation called smoothing. We argue that smoothing can be efficiently implemented by incorporating it in the search heuristic. Experimental results suggest that for learning probabilities of belief networks smoothing is helpful.

Journal ArticleDOI
TL;DR: A fast two-stage ACO algorithm is proposed in this paper, which overcomes the inherent problems of traditional ACO algorithms and is tested in maps of various complexities and compared with different algorithms.
Abstract: Ant colony optimization (ACO) algorithms are often used in robotic path planning; however, the algorithms have two inherent problems. On one hand, the distance elicitation function and transfer function are usually used to improve the ACO algorithms, whereas, the two indexes often fail to balance between algorithm efficiency and optimization effect; On the other hand, the algorithms are heavily affected by environmental complexity. Based on the scent pervasion principle, a fast two-stage ACO algorithm is proposed in this paper, which overcomes the inherent problems of traditional ACO algorithms. The basic idea is to split the heuristic search into two stages: preprocess stage and path planning stage. In the preprocess stage, the scent information is broadcasted to the whole map and then ants do path planning under the direction of scent information. The algorithm is tested in maps of various complexities and compared with different algorithms. The results show the good performance and convergence speed of the proposed algorithm, even the high grid resolution does not affect the quality of the path found.