Search

In today's world, the data generated by many applications are increasing drastically, and finding an optimal subset of features from the data has become a crucial task. The main objective of this review is to analyze and comprehend different stochastic local search algorithms to find an optimal feature subset. Simulated annealing, tabu search, genetic programming, genetic algorithm, particle swarm optimization, artificial bee colony, grey wolf optimization, and bat algorithm, which have been used in feature selection, are discussed. This review also highlights the filter and wrapper approaches for feature selection. Furthermore, this review highlights the main components of stochastic local search algorithms, categorizes these algorithms in accordance with the type, and discusses the promising research directions for such algorithms in future research of feature selection. [ABSTRACT FROM AUTHOR]

We develop a framework for the rigorous analysis of focused stochastic local search algorithms. These algorithms search a state space by repeatedly selecting some constraint that is violated in the current state and moving to a random nearby state that addresses the violation, while (we hope) not introducing many new violations. An important class of focused local search algorithms with provable performance guarantees has recently arisen from algorithmizations of the Lovász local lemma (LLL), a nonconstructive tool for proving the existence of satisfying states by introducing a background measure on the state space. While powerful, the state transitions of algorithms in this class must be, in a precise sense, perfectly compatible with the background measure. In many applications this is a very restrictive requirement, and one needs to step outside the class. Here we introduce the notion of measure distortion and develop a framework for analyzing arbitrary focused stochastic local search algorithms, recovering LLL algorithmizations as the special case of no distortion. Our framework takes as input an arbitrary algorithm of such type and an arbitrary probability measure and shows how to use the measure as a yardstick of algorithmic progress, even for algorithms designed independently of the measure. [ABSTRACT FROM AUTHOR]

Wireless ad hoc and sensor networks play an important role in providing flexible deployment and mobile connectivity for next generation network. Since there is no fixed physical backbone infrastructure, some of the nodes are selected to form a virtual backbone. Efficient algorithms for identifying the Minimum Connected Dominating Set (MCDS) have many practical applications in wireless sensor networks deployment and management. We propose two algorithms in this paper for solving the MCDS problem. The first algorithm called Memetic Algorithm for the MCDS problem, or MA-MCDS shortly. This is a new hybrid algorithm based on genetic algorithm in addition to local search strategies for the MCDS problem. In order to achieve fast performance, MA-MCDS algorithm uses local search and intensification procedures in addition to genetic operations. In the second algorithm, simulated annealing is used to enhance a stochastic local search with the ability to of run away from local solutions. In addition, we present a new objective function that effectively measure the quality of the solutions of our proposed algorithms. Both algorithms are tested using different benchmark test graph sets available in the literature, and shows good results in terms of solution quality. [ABSTRACT FROM AUTHOR]

We propose a new model for the formation of a public-private partnership mechanism, formulated as a bilevel Boolean programming problem. We show that this task is ∑2P-hard in both optimistic and pessimistic forms. We develop a stochastic iterative algorithm for solving this problem. We also present computational experiments on real information that demonstrate the capabilities of the proposed approach. [ABSTRACT FROM AUTHOR]

Metaheuristics provide high-level instructions for designing heuristic optimisation algorithms and have been successfully applied to a range of computationally hard real-world problems. Local search metaheuristics operate under a single-point based search framework with the goal of iteratively improving a solution in hand over time with respect to a single objective using certain solution perturbation strategies, known as move operators, and move acceptance methods starting from an initially generated solution. Performance of a local search method varies from one domain to another, even from one instance to another in the same domain. There is a growing number of studies on ‘more general’ search methods referred to as cross-domain search methods, or hyper-heuristics, that operate at a high-level solving characteristically different problems, preferably without expert intervention. This paper provides a taxonomy and overview of existing local search metaheuristics along with an empirical study into the effects that move acceptance methods, as components of single-point based local search metaheuristics, have on the cross-domain performance of such algorithms for solving multiple combinatorial optimisation problems. The experimental results across a benchmark of nine different computationally hard problems highlight the shortcomings of existing and well-known methods for use as components of cross-domain search methods, despite being re-tuned for solving each domain. [ABSTRACT FROM AUTHOR]

Several efficient SAT-based methods for computing the preferred extensions in (abstract) argumentation frameworks (AF) are proposed lately. However, only complete SAT solvers have been exploited so far. It is a natural question that how the appealing stochastic local search (SLS) approach could advance the performance. In this paper, we developed two SLS algorithms for computing the preferred extensions in AF, and a complete one which combines the strength of the better one with complete SAT solvers. Our first SLS algorithm Ite-CCAEP works by calling an SLS SAT solver Swcca in an iterative manner with adaptive heuristics. Our second SLS algorithm Inc-CCAEP realized an incremental version of Swcca, specially designed for computing the preferred extensions in AF. Though Ite-CCAEP and Inc-CCAEP do not guarantee completeness, they notably outperform a state-of-the-art solver consistently on most benchmarks with non-empty preferred extensions. Experimental results also show that Inc-CCAEP is more efficient than Ite-CCAEP, which inspired the design of a novel complete algorithm called CCASATEP that uses Inc-CCAEP as an efficient preprocessor. Further experiments show that CCASATEP is competitive to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Graph has become increasingly important in modeling complicated structures and data such as chemical compounds, and social networks. Recent advance of machine learning research has witnessed a number of models based on graphs, from which information retrieval study is also benefited since many of these models have been verified by different information retrieval tasks. In this paper, we investigate the issues of indexing graphs and define a novel solution by applying a stochastic local search (SLS) method to extract subgraphs that will be used for the Information Retrieval process. To reduce the size of the index, we take into consideration the size of the query and the set of the frequent subgraphs. In other words, the subgraphs that will be used to create the index will have a size equal to the size of the query in order to optimize as much as possible the search space and the execution time. The proposed method is evaluated on the CACM collection, which contains the titles, authors and abstracts (where available) of a set of scientific articles and a set of queries and relevancy judgments and compared to the vector-based cosine model proposed in the literature. Our method is able to discover frequent subgraphs serving to establish the index and relevant documents for the Information Retrieval process’s output. The numerical results show that our method provides competitive results and finds high quality solutions (documents) compared to the relevant documents cited on the CACM collection. [ABSTRACT FROM AUTHOR]

We consider a new load balancing model that arises in the processing of user requests for files located on a given set of servers. The optimization criterion is the total excess of actual load over the limit load. In order to redistribute the load and minimize the criterion, files can be moved between the servers. We show that if there are no other constraints related to the stage of moving the files, then this problem is equivalent to a problem previously considered in literature. For this special case of this problem, we propose a stochastic local search scheme that combines a special procedure for fast querying of the neighborhoods and a procedure of non-aggravating modification of intermediate solutions. Results of numerical experiments show that the proposed approach is able to find high-quality solutions for instances of large dimension under tight time constraints. [ABSTRACT FROM AUTHOR]

Optimization of multi-modal functions is challenging even for evolutionary and swarm-based algorithms as it requires an efficient exploration for finding the promising region of the search space, and effective exploitation to precisely find the global optimum. Grey Wolf Optimizer (GWO) is a recently developed metaheuristic algorithm that is inspired by nature with a relatively small number of parameters for tuning. However, GWO and most of its variants may suffer from the lack of population diversity, premature convergence, and the inability to preserve a good balance between exploratory and exploitative behaviors. To address these limitations, this work proposes a new variant of GWO incorporating memory, evolutionary operators, and a stochastic local search technique. It further integrates Linear Population Size Reduction (LPSR) technique. The proposed algorithm is comprehensively tested on 23 numerical benchmark functions, high dimensional benchmark functions, 13 engineering case studies, four data classifications, and three function approximation problems. The benchmark functions are mostly taken from the CEC 2005 and CEC 2010 special sessions, and they include rotated, shifted functions. The engineering case studies are from the CEC 2020 real-world non-convex constrained optimization problems. The performance of the proposed GWO is compared with popular metaheuristics, namely, particle swarm optimization (PSO), gravitational search algorithm (GSA), slap swarm algorithm (SSA), differential evolution (DE), self-adaptive differential evolution (SADE), basic GWO and its three recently improved variants. Statistical analysis and Friedman tests have been conducted to thoroughly compare their performance. The obtained results demonstrate that the proposed GWO outperforms the algorithms compared for the benchmark functions and engineering case studies tested. [ABSTRACT FROM AUTHOR]

The maximum vertex weight clique problem (MVWCP) is an important generalization of the maximum clique problem (MCP) that has a wide range of real-world applications. In situations where rigorous guarantees regarding the optimality of solutions are not required, MVWCP is usually solved using stochastic local search (SLS) algorithms, which also define the state of the art for solving this problem. However, there is no single SLS algorithm that gives the best performance across all classes of MVWCP instances, and it is challenging to effectively identify the most suitable algorithm for each class of MVWCP instances. In this work, we follow the paradigm of Programming by Optimization (PbO) to develop a new, flexible and highly parametric SLS framework for solving MVWCP, combining, for the first time, a broad range of effective heuristic mechanisms. By automatically configuring this PbO-MWC framework, we achieve substantial advances in the state of the art in solving MVWCP over a broad range of prominent benchmarks, including two derived from real-world applications in transplantation medicine (kidney exchange) and assessment of research excellence. • A highly parametric local search framework for maximum weight clique problem. • Automatically configuring this framework. • Achieve substantial advances over many prominent benchmarks. • Suggesting effectiveness of heuristic mechanisms on different benchmarks. [ABSTRACT FROM AUTHOR]

Automating high school timetabling is a challenging task. This problem is a well known hard computational problem which has been of interest to practitioners as well as researchers. High schools need to timetable their regular activities once per year, or even more frequently. The exact solvers might fail to find a solution for a given instance of the problem. A selection hyper-heuristic can be defined as an easy-to-implement, easy-to-maintain and effective 'heuristic to choose heuristics' to solve such computationally hard problems. This paper describes the approach of the team hyper-heuristic search strategies and timetabling (HySST) to high school timetabling which competed in all three rounds of the third international timetabling competition. HySST generated the best new solutions for three given instances in Round 1 and gained the second place in Rounds 2 and 3. It achieved this by using a fairly standard stochastic search method but significantly enhanced by a selection hyper-heuristic with an adaptive acceptance mechanism. [ABSTRACT FROM AUTHOR]

Designing high-performance solvers for computationally hard problems is a difficult and often time-consuming task. Although such design problems are traditionally solved by the application of human expertise, we argue instead for the use of automatic methods. In this work, we consider the design of stochastic local search (SLS) solvers for the propositional satisfiability problem (SAT). We first introduce a generalized, highly parameterized solver framework, dubbed SATenstein, that includes components drawn from or inspired by existing high-performance SLS algorithms for SAT. The parameters of SATenstein determine which components are selected and how these components behave; they allow SATenstein to instantiate many high-performance solvers previously proposed in the literature, along with trillions of novel solver strategies. We used an automated algorithm configuration procedure to find instantiations of SATenstein that perform well on several well-known, challenging distributions of SAT instances. Our experiments show that SATenstein solvers achieved dramatic performance improvements as compared to the previous state of the art in SLS algorithms; for many benchmark distributions, our new solvers also significantly outperformed all automatically tuned variants of previous state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the capabilities of three new local search (LS) metaheuristic algorithms in determining solutions to an annual crop planning (ACP) problem at an existing Irrigation Scheme. ACP is an optimisation problem in agricultural planning which involves determining resource allocation solutions amongst the various crops that are required to be grown at an irrigation scheme, within a year. The LS algorithms investigated are the best performance algorithm (BPA), the iterative best performance algorithm (IBPA) and the largest absolute difference algorithm (LADA). To determine the relative merits of the solutions found by these algorithms, their solutions have been compared against the solutions of two well-known LS metaheuristic algorithms and four population-based metaheuristic algorithms in the literature. The results show that BPA, IBPA and LADA were competitive in determining solutions for this particular optimisation problem. [ABSTRACT FROM AUTHOR]

Local search algorithms exploit moves on an adjacency graph of the search space. An "elementary landscape" exists if the objective function f is an eigenfunction of the Laplacian of the graph induced by the neighborhood operator; this allows various statistics about the neighborhood to be computed in closed form. A new component based model makes it relatively simple to prove that certain types of landscapes are elementary. The traveling salesperson problem, weighted graph (vertex) coloring and the minimum graph bisection problem yield elementary landscapes under commonly used local search operators. The component model is then used to efficiently compute the mean objective function value over partial neighborhoods for these same problems. For a traveling salesperson problem over n cities, the 2-opt neighborhood can be decomposed into [n/2-1] [n/2-1] partial neighborhoods. For graph coloring and the minimum graph bisection problem, partial neighborhoods can be used to focus search on those moves that are capable of producing a solution with a strictly improving objective function value. [ABSTRACT FROM AUTHOR]

This paper proposed an effective modified cuckoo search (MCS) algorithm based on stochastic local search method to solve engineering structural design optimization problems. The algorithm used the inertia weight to balance the exploration and exploitation ability of algorithm. And it introduced the stochastic local search technique to improve the convergence speed of CS algorithm. The proposed algorithm was applied to solve two engineering structural design optimization problems. The result shows that MCS is of better or competitive performances when it compares with other existing optimization methods. [ABSTRACT FROM AUTHOR]

Boolean Satisfiability problem (SAT) plays a prominent role in many domains of computer science and artificial intelligence due to its significant importance in both theory and applications. Algorithms for solving SAT problems can be categorized into two main classes: complete algorithms and incomplete algorithms (typically stochastic local search (SLS) algorithms). SLS algorithms are among the most effective for solving uniform random SAT problems, while hybrid algorithms achieved great breakthroughs for solving hard random SAT (HRS) problem recently. However, there is a lack of algorithms that can effectively solve both uniform random SAT and HRS problems. In this paper, a new SLS algorithm named SelectNTS is proposed aiming at solving both uniform random SAT and HRS problem effectively. SelectNTS is essentially an improved probability selection based local search algorithm, the core of which includes new clause and variable selection heuristics: a new clause weighting scheme and a biased random walk strategy are utilized to select a clause, while a new probability selection strategy with the variation of configuration checking strategy is used to select a variable. Extensive experimental results show that SelectNTS outperforms the state-of-the-art random SAT algorithms and hybrid algorithms in solving both uniform random SAT and HRS problems effectively. [ABSTRACT FROM AUTHOR]

Abstract: We present experimental results on benchmark problems in 3D cubic lattice structures with the Miyazawa–Jernigan energy function for two local search procedures that utilise the pull-move set: (i) population-based local search (PLS) that traverses the energy landscape with greedy steps towards (potential) local minima followed by upward steps up to a certain level of the objective function; (ii) simulated annealing with a logarithmic cooling schedule (LSA). The parameter settings for PLS are derived from short LSA-runs executed in pre-processing and the procedure utilises tabu lists generated for each member of the population. In terms of the total number of energy function evaluations both methods perform equally well, however, PLS has the potential of being parallelised with an expected speed-up in the region of the population size. Furthermore, both methods require a significant smaller number of function evaluations when compared to Monte Carlo simulations with kink-jump moves. [Copyright &y& Elsevier]

Abstract: A method for finding all roots of a system of nonlinear equations is described. Our method makes use of C-GRASP, a recently proposed continuous global optimization heuristic. Given a nonlinear system, we solve a corresponding adaptively modified global optimization problem multiple times, each time using C-GRASP, with areas of repulsion around roots that have already been found. The heuristic makes no use of derivative information. We illustrate the approach using systems found in the literature. [Copyright &y& Elsevier]

Abstract: We present results from three-dimensional protein folding simulations in the HP-model on ten benchmark problems. The simulations are executed by a simulated annealing-based algorithm with a time-dependent cooling schedule. The neighbourhood relation is determined by the pull-move set. The results provide experimental evidence that the maximum depth D of local minima of the underlying energy landscape can be upper bounded by . The local search procedure employs the stopping criterion , where m is an estimation of the average number of neighbouring conformations, relates to the mean of non-zero differences of the objective function for neighbouring conformations, and is the confidence that a minimum conformation has been found. The bound complies with the results obtained for the ten benchmark problems. [Copyright &y& Elsevier]

Abstract: Stochastic local search (SLS) algorithms have recently been proven to be among the best approaches to solving computationally hard problems. SLS algorithms typically have a number of parameters, optimized empirically, that characterize and determine their performance. In this article, we focus on the noise parameter. The theoretical foundation of SLS, including an understanding of how to the optimal noise varies with problem difficulty, is lagging compared to the strong empirical results obtained using these algorithms. A purely empirical approach to understanding and optimizing SLS noise, as problem instances vary, can be very computationally intensive. To complement existing experimental results, we formulate and analyze several Markov chain models of SLS in this article. In particular, we compute expected hitting times and show that they are rational functions for individual problem instances as well as their mixtures. Expected hitting time curves are analytical counterparts to noise response curves reported in the experimental literature. Hitting time analysis using polynomials and convex functions is also discussed. In addition, we present examples and experimental results illustrating the impact of varying noise probability on SLS run time. In experiments, where most probable explanations in Bayesian networks are computed, we use synthetic problem instances as well as problem instances from applications. We believe that our results provide an improved theoretical understanding of the role of noise in stochastic local search, thereby providing a foundation for further progress in this area. [Copyright &y& Elsevier]

The design of large libraries of oligonucleotides having constant GC-content and satisfying Hamming distance constraints between oligonucleotides and their Watson-Crick complements is important in reducing hybridization errors in DNA computing, DNA microarray technologies, and molecular bar coding. Various techniques have been studied for the construction of such oligonucleotide libraries, ranging from algorithmic constructions via stochastic local search to theoretical constructions via coding theory A new stotchastic local search method is introduced, which yields improvements for more than one third of the benchmark lower bounds of Gaborit and King (2005) for n-mer oligonucleotide libraries when n ≤14. Several optimal libraries are also found by computing maximum cliques on certain graphs. [ABSTRACT FROM AUTHOR]

Abstract: While recent research on rule learning has focused largely on finding highly accurate hypotheses, we evaluate the degree to which these hypotheses are also simple, that is small. To realize this, we compare well-known rule learners, such as CN2, RIPPER, PART, FOIL and C5.0 rules, with the benchmark system SL2 that explicitly aims at computing small rule sets with few literals. The results show that it is possible to obtain a similar level of accuracy as state-of-the-art rule learners using much smaller rule sets. [Copyright &y& Elsevier]

In this work, we consider a complex flowshop scheduling problem in which both no-wait and separate setup times are considered. The optimisation criterion is the minimisation of the total completion time. We propose an effective dominance rule for the four machine case that can also be used for m machines. Five simple and fast heuristics are proposed along with two easy to code stochastic local search methods, one of them being based on Iterated Local Search (ILS). An extensive computational evaluation is carried out with two sets of 5,400 instances. All seven methods are compared to two recent algorithms. The results, confirmed by thorough statistical analyses, show that the proposed methods are more effective and efficient when compared to the best existing algorithms in the literature for the considered problem. [ABSTRACT FROM AUTHOR]

Abstract: We prove a (m/δ)O(κ) · n a time bound for finding minimum solutions S min of Feature Set problems, where n is the total size of a given Feature Set problem, κ ⩽∣S min∣, m equals the number of non-target features, a is a (relatively small) constant, and 1− δ is the confidence that the solution is of minimum length. In terms of parameterized complexity of NP-complete problems, our time bound differs from an FPT-type bound by the factor m O(κ) for fixed δ. The algorithm is applied to a prominent microarray dataset: The classification of gene-expression data related to acute myeloid leukaemia (AML) and acute lymphoblastic leukaemia (ALL). From the set of potentially significant features calculated by the algorithm we can identify three genes (D88422, M92287, L09209) that produce zero errors on the test set by using a simple, straightforward evaluation procedure (performing the test on the single gene M84526 produces only one error). [Copyright &y& Elsevier]

Cost-based abduction (CBA) is an important problem in reasoning under uncertainty, and can be considered a generalization of belief revision. CBA is known to be NP-hard and has been a subject of considerable research over the past decade. In this paper, we investigate the fitness landscape for CBA, by looking at fitness–distance correlation for local minima and at landscape ruggedness. Our results indicate that stochastic local search techniques would be promising on this problem. We go on to present an iterated local search algorithm based on hill-climbing, tabu search, and simulated annealing. We compare the performance of our algorithm to simulated annealing, and to Santos' integer linear programming method for CBA. [ABSTRACT FROM AUTHOR]

The boolean satisfiability problem (SAT) is stated as follows: given a boolean formula in CNF, find a truth assignment that satisfies its clauses. In this paper, we present a general framework based on stochastic local search and the structure of the CNF formula for solving incremental SAT problems. Given a satisfiable boolean formula in CNF, incremental SAT consists of checking whether the satisfiability is preserved when new clauses are added to the current clause set and if not, look for a new assignment that satisfies the old clauses and the new ones. The results of the experimentation we have conducted demonstrate the efficiency of our method to deal with large randomly generated incremental SAT problems. [ABSTRACT FROM AUTHOR]

A main challenge when designing constraint based systems in general and those involving temporal constraints in particular, is the ability to deal with constraints in a dynamic and evolutive environment. That is to check, anytime a new constraint is added, whether a consistent scenario continues to be consistent when a new constraint is added and if not, whether a new scenario satisfying the old and new constraints can be found. We talk then about on line temporal constraint based systems capable of reacting, in an efficient way, to any new external information during the constraint resolution process. In this paper, we will investigate the applicability of systematic versus approximation methods for solving incremental temporal constraint problems. In order to handle both numeric and symbolic constraints, the systematic method is based on constraint propagation performed at both the qualitative and quantitative levels. The approximation methods are respectively based on stochastic local search and genetic algorithms. Experimental evaluation of the performance in time and the quality of the solution returned (number of violated constraints) of the different techniques has been performed on randomly generated temporal constraint problems. The results favour the exact method for problems with reasonable size while the approximation techniques are the methods of choice for very large problems in the case where we want to trade the quality of the solution for the process time. Indeed, while the approximation methods are faster for large problems, they do not guarantee, in general, the completeness of the solution returned. [ABSTRACT FROM AUTHOR]

The function of many RNAs depends crucially on their structure. Therefore, the design of RNA molecules with specific structural properties has many potential applications, e.g. in the context of investigating the function of biological RNAs, of creating new ribozymes, or of designing artificial RNA nanostructures. Here, we present a new algorithm for solving the following RNA secondary structure design problem: given a secondary structure, find an RNA sequence (if any) that is predicted to fold to that structure. Unlike the (pseudoknot-free) secondary structure prediction problem, this problem appears to be hard computationally. Our new algorithm, “RNA Secondary Structure Designer (RNA-SSD)”, is based on stochastic local search, a prominent general approach for solving hard combinatorial problems. A thorough empirical evaluation on computationally predicted structures of biological sequences and artificially generated RNA structures as well as on empirically modelled structures from the biological literature shows that RNA-SSD substantially outperforms the best known algorithm for this problem, RNAinverse from the Vienna RNA Package. In particular, the new algorithm is able to solve structures, consistently, for which RNAinverse is unable to find solutions. The RNA-SSD software is publically available under the name of RNA Designer at the RNASoft website (www.rnasoft.ca). [Copyright &y& Elsevier]

• Combinatorial optimisation in radiation therapy for cancer treatment. • Deliverable treatment plans solving the Direct Aperture Optimisation problem. • Competitive Stochastic local search algorithm for Direct Aperture Optimisation. • Fluence-map representation for the Direct Aperture Optimisation Problem. Intensity modulated radiation therapy (IMRT) is a widely applied cancer treatment technique that aims at effectively deliver radiation to cancerous cells while sparing surrounding healthy tissues. To this end, the radiation coming from a linear accelerator is modulated using a physical device called multi-leaf collimator. Traditionally, a sequential approach is applied to generate treatment plans in IMRT. In this approach, the optimal intensities for each beam angle are computed (Fluence Map Optimisation, FMO), and then a sequencing problem is solved to obtain the collimator setup (aperture shapes) required to achieve the intensities computed for the FMO problem. The sequencing step is critical as the treatment plans computed to solve the FMO problem are not clinically acceptable. Unfortunately, treatment plans obtained by the sequencing step are severely impaired w.r.t. those obtained in the FMO step. One approach that aims to deal with the problem described above is the Direct Aperture Optimisation (DAO) approach. The DAO problem aims at simultaneously determining deliverable aperture shapes and a set of radiation intensities. This approach considers both physical and delivery time constraints, allowing to generate clinically acceptable treatment plans. In this paper, we propose a stochastic local search algorithm to solve the DAO problem. Our approach alternates the search between two neighbourhood definitions; the first focused on the fluence map (intensities) and the second on the aperture shapes. We analyse the algorithmic components, parameters, and overall results obtained by our algorithm on a set of clinical prostate cancer cases. We compare the obtained treatment plans to the ones obtained by the traditional two-step approach. Results show that our algorithm is able to find deliverable treatment plans without major impairments in treatment plans quality. [ABSTRACT FROM AUTHOR]

The Maximum Satisfiability (Maximum Satisfiability (MaxSAT)) approach is the choice, and perhaps the only one, to deal with most real-world problems as most of them are unsatisfiable. Thus, the search for a complete and consistent solution to a real-world problem is impractical due to computational and time constraints. As a result, MaxSAT problems and solving techniques are of exceptional interest in the domain of Satisfiability (Satisfiability (SAT)). Our research experimentally investigated the performance gains of extending the most recently developed SAT dynamic Initial Weight assignment technique (InitWeight) to handle the MaxSAT problems. Specifically, we first investigated the performance gains of dynamically assigning the initial weights in the Divide and Distribute Fixed Weights solver (DDFW+Initial Weight for Maximum Satisfiability (DDFW+InitMaxSAT)) over Divide and Distribute Fixed Weights solver (DDFW) when applied to solve a wide range of well-known unweighted MaxSAT problems obtained from DIMACS. Secondly, we compared DDFW+InitMaxSAT's performance against three known state-of-the-art SAT solving techniques: YalSAT, ProbSAT, and Sparrow. We showed that the assignment of dynamic initial weights increased the performance of DDFW+InitMaxSAT against DDFW by an order of magnitude on the majority of problems and performed similarly otherwise. Furthermore, we showed that the performance of DDFW+InitMaxSAT was superior to the other state-of-the-art algorithms. Eventually, we showed that the InitWeight technique could be extended to handling partial MaxSAT with minor modifications. [ABSTRACT FROM AUTHOR]

This note summarizes a dissertation that focuses on methods for solving structured real-world problems based on propositional satisfiability (SAT). The work contributes to the analysis and development of both complete search (DPLL and clause learning) and stochastic local search methods for SAT. [ABSTRACT FROM AUTHOR]

The use of data issued from high throughput technologies in drug target problems is widely widespread during the last decades. This study proposes a meta-heuristic framework using stochastic local search (SLS) combined with random forest (RF) where the aim is to specify the most important genes and proteins leading to the best classification of Acute Myeloid Leukemia (AML) patients. First we use a stochastic local search meta-heuristic as a feature selection technique to select the most significant proteins to be used in the classification task step. Then we apply RF to classify new patients into their corresponding classes. The evaluation technique is to run the RF classifier on the training data to get a model. Then, we apply this model on the test data to find the appropriate class. We use as metrics the balanced accuracy (BAC) and the area under the receiver operating characteristic curve (AUROC) to measure the performance of our model. The proposed method is evaluated on the dataset issued from DREAM 9 challenge. The comparison is done with a pure random forest (without feature selection), and with the two best ranked results of the DREAM 9 challenge. We used three types of data: only clinical data, only proteomics data, and finally clinical and proteomics data combined. The numerical results show that the highest scores are obtained when using clinical data alone, and the lowest is obtained when using proteomics data alone. Further, our method succeeds in finding promising results compared to the methods presented in the DREAM challenge. [ABSTRACT FROM AUTHOR]

For hard computational problems, stochastic local search has proven to be a competitive approach to finding optimal or approximately optimal problem solutions. Two key research questions for stochastic local search algorithms are: Which algorithms are effective for initialization? When should the search process be restarted? In the present work, we investigate these research questions in the context of approximate computation of most probable explanations (MPEs) in Bayesian networks (BNs). We introduce a novel approach, based on the Viterbi algorithm, to explanation initialization in BNs. While the Viterbi algorithm works on sequences and trees, our approach works on BNs with arbitrary topologies. We also give a novel formalization of stochastic local search, with focus on initialization and restart, using probability theory and mixture models. Experimentally, we apply our methods to the problem of MPE computation, using a stochastic local search algorithm known as Stochastic Greedy Search. By carefully optimizing both initialization and restart, we reduce the MPE search time for application BNs by several orders of magnitude compared to using uniform at random initialization without restart. On several BNs from applications, the performance of Stochastic Greedy Search is competitive with clique tree clustering, a state-of-the-art exact algorithm used for MPE computation in BNs. [ABSTRACT FROM AUTHOR]