The paper examines some of the processes of the closely knit relationship between Google’s ideologies of neutrality and objectivity and global market dominance. Neutrality construction comprises an important element sustaining the company’s economic position and is reflected in constant updates, estimates and changes to utility and relevance of search results. Providing a purely technical solution to these issues proves to be increasingly difficult without a human hand in steering algorithmic solutions. Search relevance fluctuates and shifts through continuous tinkering (...) and tweaking of the search algorithm. The company also uses third parties to hire human raters for performing quality assessments of algorithmic updates and adaptations in linguistically and culturally diverse global markets. The adaptation process contradicts the technical foundations of the company and calculations based on the initial Page Rank algorithm. Annual market reports, Google’s Search Quality Rating Guidelines, and reports from media specialising in search engine optimisation business are analysed. The Search Quality Rating Guidelines document provides a rare glimpse into the internal architecture of search algorithms and the notions of utility and relevance which are presented and structured as neutral and objective. Intertwined layers of ideology, hidden labour of human raters, advertising revenues, market dominance and control are discussed throughout the paper. (shrink)

The feature selection (FS) process has an essential effect in solving many problems such as prediction, regression, and classification to get the optimal solution. For solving classification problems, selecting the most relevant features of a dataset leads to better classification accuracy with low training time. In this work, a hybrid binary crow search algorithm (BCSA) based quasi-oppositional (QO) method is proposed as an FS method based on wrapper mode to solve a classification problem. The QO method was employed (...) in tuning the value of flight length in the BCSA which is controlling the ability of the crows to find the optimal solution. To evaluate the performance of the proposed method, four benchmark datasets have been used which are human intestinal absorption, HDAC8 inhibitory activity (IC50), P-glycoproteins, and antimicrobial. Accordingly, the experimental results are discussed and compared against other standard algorithms based on the accuracy rate, the average number of selected features, and running time. The results have proven the robustness of the proposed method relied on the high obtained value of accuracy (84.93–95.92%), G-mean (0.853–0.971%), and average selected features (4.36–11.8) with a relatively low computational time. Moreover, to investigate the effectiveness of the proposed method, Friedman test was used which declared that the performance supremacy of the proposed BCSA-QO with four datasets was very evident against BCSA and CSA by selecting the minimum relevant features and producing the highest accuracy classification rate. The obtained results verify the usefulness of the proposed method (BCSA-QO) in the FS with classification in terms of high classification accuracy, a small number of selected features, and low computational time. (shrink)

In the study of rational solutions to polynomial equations in two-variables, we show that an algorithmic solution to the decision problem (existence of solutions) enables one to construct a search algorithm for all solutions.

Multiple sequence alignment is one of the major problems that can be encountered in the bioinformatics field. MSA consists in aligning a set of biological sequences to extract the similarities between them. Unfortunately, this problem has been shown to be NP-hard. In this article, a new algorithm was proposed to deal with this problem; it is based on a quantum-inspired cuckoo search algorithm. The other feature of the proposed approach is the use of a randomized progressive alignment (...) method based on a hybrid global/local pairwise algorithm to construct the initial population. The results obtained by this hybridization are very encouraging and show the feasibility and effectiveness of the proposed solution. (shrink)

A Bayesian network consists of two distinct parts: a directed acyclic graph (DAG or belief-network structure) and a set of parameters for the DAG. The DAG in a Bayesian network can be used to represent both causal hypotheses and sets of probability distributions. Under the causal interpretation, a DAG represents the causal relations in a given population with a set of vertices V when there is an edge from A to B if and only if A is a direct cause (...) of B relative to V. (We adopt the convention that sets of variables are capitalized and boldfaced, and individual variables are capitalized and italicized.) Under the statistical interpretation a DAG G can be taken to represent a set of all distributions all of which share a set of conditional independence relations that are entailed by satisfying a local directed Markov property (defined below). (shrink)

The hybridization of two or more energy sources into a single power station is one of the widely discussed solutions to address the demand and supply havoc generated by renewable production, heating power, and cooling power) and its energy storage issues. Hybrid energy sources work based on the complementary existence of renewable sources. The combined cooling, heating, and power is one of the significant systems and shows a profit from its low environmental impact, high energy efficiency, low economic investment, and (...) sustainability in the industry. This paper presents an economic model of a microgrid system containing the CCHP system and energy storage considering the energy coupling and conversion characteristics, the effective characteristics of each microsource, and energy storage unit is proposed. The random forest regression model was optimized by the gravitational search algorithm. The test results show that the GSA-RFR model improves prediction accuracy and reduces the generalization error. The detail of the MG network and the energy storage architecture connected to the other renewable energy sources is discussed. The mathematical formulation of energy coupling and energy flow of the MG network including wind turbines, photovoltaic, CCHP system, fuel cell, and energy storage devices are presented. The testing system has been analysed under load peak cutting and valley filling of energy utilization index, energy utilization rate, the heat pump, the natural gas consumption of the microgas turbine, and the energy storage unit. The energy efficiency costs were observed as 88.2% and 86.9% with heat pump and energy storage operation comparing with GSA-RFR-based operation costs as 93.2% and 93% in summer and winter season, respectively. The simulation results extended the rationality and economy of the proposed model. (shrink)

This paper applies a proposed modified stochastic fractal search algorithm (MSFS) for dealing with all constraints of optimal reactive power dispatch (ORPD) and finding optimal solutions for three different cases including power loss optimization, voltage deviation optimization, and L-index optimization. The proposed MSFS method is newly constructed in the paper by modifying three new solution update mechanisms on standard stochastic fractal search algorithm (SSFS). The first modification is to keep only one formula and abandon one formula (...) in the diffusion process while the second modification and the third modification are used in the first update and the second update. In two updates of SSFS, solutions with low quality are updated with high probability while other solutions with high quality do not get chances to be updated. This manner results in the fact that some promising solutions around the high quality solutions can be missed. In order to tackle this restriction, the second modification of MSFS is to newly update the worst solutions in the first update and the best solutions in the second update. In the third modification, all existing formulas of SSFS in the two updates are abandoned and the same new proposed technique is used for updating such solutions in two updates. Compared to SSFS, the three modifications can bring advantages to MSFS such as using smaller number of produced solutions per iteration, spending shorter execution time, finding better optimal solutions, and owning more stable search ability. Furthermore, the proposed method also sees its effectiveness and robustness over SSFS by testing on IEEE 30-bus system and IEEE 118-bus system with three different single objectives for each system. The proposed method can find less minimum, average, and maximum for all the cases in addition to faster search speed. Besides, the proposed method is also compared to other methods such as PSO-based method group, GA-based method group, DE-based method group, and other recent methods. Result comparisons also indicate that the proposed method can be more efficient than almost all these methods with respect to less minimum and smaller values of control parameters. As a result, evaluation of the performance of the proposed method is that it should be used for seeking solutions of ORPD problem. (shrink)

We consider a special case of heuristics, namely numeric heuristic evaluation functions, and their use in artificial intelligence search algorithms. The problems they are applied to fall into three general classes: single-agent path-finding problems, two-player games, and constraint-satisfaction problems. In a single-agent path-finding problem, such as the Fifteen Puzzle or the travelling salesman problem, a single agent searches for a shortest path from an initial state to a goal state. Two-player games, such as chess and checkers, involve an adversarial (...) relationship between two players, each trying to win the game. In a constraint-satisfaction, problem, such as the 8-Queens problem, the task is to find a state that satisfies a set of constraints. All of these problems are computationally intensive, and heuristic evaluation functions are used to reduce the amount of computation required to solve them. In each case we explain the nature of the evaluation functions used, how they are used in search algorithms, and how they can be automatically learned or acquired. (shrink)

This article mitigates the challenges of previously reported literature by reducing the operating cost and improving the performance of network. A genetic algorithm-based tabu search methodology is proposed to solve the link capacity and traffic allocation problem in a computer communication network. An efficient modern super-heuristic search method is used to influence the fixed cost, delay cost, and variable cost of a link on the total operating cost in the computer communication network are discussed. The article analyses (...) a large number of computer simulation results to verify the effectiveness of the tabu search algorithm for CFA problems and also improves the quality of solutions significantly compared with traditional Lagrange relaxation and subgradient optimization algorithms. The experimental results show that with the increase of the weighted coefficient of variable cost, the proportion of variable cost in the total cost increases from 10 to 35%. The growth is relatively slow, and the fixed cost is still the main component. In addition, due to the increase in the variable cost, the tabu search algorithm will also choose the link with large luxury to reduce the variable cost, which makes the fixed cost slightly increase, while the network delay cost and average delay slightly decrease. The proposed method, when compared with the genetic algorithm, has more advantages for large-scale or heavy-load networks. (shrink)

Among the data clustering algorithms, the k-means (KM) algorithm is one of the most popular clustering techniques because of its simplicity and efficiency. However, KM is sensitive to initial centers and it has a local optima problem. The k-harmonic means (KHM) clustering algorithm solves the initialization problem of the KM algorithm, but it also has a local optima problem. In this paper, we develop a new algorithm for solving this problem based on a modified version of (...) particle swarm optimization (MPSO) algorithm and KHM clustering. In the proposed algorithm, MPSO is equipped with the cuckoo search algorithm and two new concepts used in PSO in order to improve the efficiency, fast convergence, and escape from local optima. MPSO updates the positions of particles based on a combination of global worst, global best with personal worst, and personal best to dynamically be used in each iteration of the MPSO. The experimental result on eight real-world data sets and two artificial data sets confirms that this modified version is superior to KHM and the regular PSO algorithm. The results of the simulation show that the new algorithm is able to create promising solutions with fast convergence, high accuracy, and correctness while markedly improving the processing time. (shrink)

This research proposes a modified metaheuristic optimization algorithm, named as improved stochastic fractal search, which is formed based on the integration of the quasiopposition-based learning and chaotic local search schemes into the original SFS algorithm for solving the optimal capacitor placement in radial distribution networks. The test problem involves the determination of the optimal number, location, and size of fixed and switched capacitors at different loading conditions so that the network total yearly cost is minimized with (...) simultaneous fulfillment of operating constraints. Also, the hourly on/off scheduling plans of switched shunt capacitors considering a modified cost objective function are obtained. The proposed ISFS algorithm has been tested on two IEEE 69-bus and 119-bus RDNs and a practical 152-bus RDN. For clarifying the effectiveness and validation of the ISFS, the simulated results have been compared with those of other previously utilized solution approaches in the literature as well as the original SFS. From result comparison, the proposed ISFS outperforms other previous approaches regarding solution quality and statistical performance for the compared cases, especially in the complex and large-scale networks. Notably, compared with the original SFS, the proposed ISFS shows a significantly better performance in all the tested cases. (shrink)

Harmony Search Algorithm (HSA) is an evolutionary algorithm which mimics the process of music improvisation to obtain a nice harmony. The algorithm has been successfully applied to solve optimization problems in different domains. A significant shortcoming of the algorithm is inadequate exploitation when trying to solve complex problems. The algorithm relies on three operators for performing improvisation: memory consideration, pitch adjustment, and random consideration. In order to improve algorithm efficiency, we use roulette wheel (...) and tournament selection in memory consideration, replace the pitch adjustment and random consideration with a modified polynomial mutation, and enhance the obtained new harmony with a modified β-hill climbing algorithm. Such modification can help to maintain the diversity and enhance the convergence speed of the modified HS algorithm. β-hill climbing is a recently introduced local search algorithm that is able to effectively solve different optimization problems. β-hill climbing is utilized in the modified HS algorithm as a local search technique to improve the generated solution by HS. Two algorithms are proposed: the first one is called PHSβ–HC and the second one is called Imp. PHSβ–HC. The two algorithms are evaluated using 13 global optimization classical benchmark function with various ranges and complexities. The proposed algorithms are compared against five other HSA using the same test functions. Using Friedman test, the two proposed algorithms ranked 2nd (Imp. PHSβ–HC) and 3rd (PHSβ–HC). Furthermore, the two proposed algorithms are compared against four versions of particle swarm optimization (PSO). The results show that the proposed PHSβ–HC algorithm generates the best results for three test functions. In addition, the proposed Imp. PHSβ–HC algorithm is able to overcome the other algorithms for two test functions. Finally, the two proposed algorithms are compared with four variations of differential evolution (DE). The proposed PHSβ–HC algorithm produces the best results for three test functions, and the proposed Imp. PHSβ–HC algorithm outperforms the other algorithms for two test functions. In a nutshell, the two modified HSA are considered as an efficient extension to HSA which can be used to solve several optimization applications in the future. (shrink)

Shane Harwood and Richard Scheines. Genetic Algorithm Search Over Causal Models.

PurposeTo investigate how search engine users manipulate the rankings of search results. Search engines employ different ranking methods in order to display the “best” results first. One of the ranking methods is PageRank, where the number of links pointing to the page influences its rank. The “anchor text,” the clickable text of the hypertext link is another “ingredient” in the ranking method. There are a number of cases where the public challenged the Google's ranking, by creating a (...) so‐called “Google bomb” – creating links to pages they wanted to be highly ranked for given query. Google is chosen as the search engine, because it is currently by far the most popular search engine.Design/methodology/approachPageRank, one of the major parameters of Google's ranking algorithm is described, and the author explains how this algorithm is exploited by communities of users to promote a certain web page for a specific query. This process is called “Google bombing.” Recent reaction of Google to this phenomenon is also described.FindingsSpecific examples of “accomplished Google bombs” show that the public is able to manipulate search results.Originality/valueGoogle, instead of being an unobtrusive information retrieval tool has become highly influential in the web scenery. Some users pay for search engine optimization, while others utilize the power of the crowd to influence Google's rankings. This paper supports the claims of Introna and Nissenbaum regarding the power of search engines. (shrink)

This paper introduce a new variant of the Genetic Algorithm whichis developed to handle multivariable, multi-objective and very high search space optimization problems like the solving system of non-linear equations. It is an integer coded Genetic Algorithm with conventional cross over and mutation but with Inverse algorithm is varying its search space by varying its digit length on every cycle and it does a fine search followed by a coarse search. And its solution (...) to the optimization problem will converge to precise value over the cycles. Every equation of the system is considered as a single minimization objective function. Multiple objectives are converted to a single fitness function by summing their absolute values. Some difficult test functions for optimization and applications are used to evaluate this algorithm. The results prove that this algorithm is capable to produce promising and precise results. (shrink)

In this study, two modified gradient descent algorithms are proposed for time-delayed models. To estimate the parameters and time-delay simultaneously, a redundant rule method is introduced, which turns the time-delayed model into an augmented model. Then, two GD algorithms can be used to identify the time-delayed model. Compared with the traditional GD algorithms, these two modified GD algorithms have the following advantages: avoid a high-order matrix eigenvalue calculation, thus, are more efficient for large-scale systems; have faster convergence rates, therefore, are (...) more practical in engineering practices. The convergence properties and simulation examples are presented to illustrate the efficiency of the two algorithms. (shrink)

The paper discusses the problem of assignment production resources in executing a production order on the example of the car rims manufacturing process. The more resources are involved in implementing the manufacturing process and the more they can be used interchangeably, the more complex and problematic the scheduling process becomes. Special attention is paid to the effective scheduling and assignment of rim machining operations to production stations in the considered manufacturing process. In this case, the use of traditional scheduling methods (...) based on simple calculations, or the know-how of process engineers often turns out to be insufficient to achieve the intended results. Due to the scale of the problems faced in practice, the methods based on approximate approaches (Genetic and Tabu Search) were used to solve them. In this perspective, the problem under consideration involves the extension of the classic assignment problem with the possibility of taking into account: the times of operations, potential changeovers and the capacity of production resources. (shrink)

Combining the communicative language competence model and the perspective of multimodal research, this research proposes a research framework for oral communicative competence under the multimodal perspective. This not only truly reflects the language communicative competence but also fully embodies the various contents required for assessment in the basic attributes of spoken language. Aiming at the feature sparseness of the user evaluation matrix, this paper proposes a feature weight assignment algorithm based on the English spoken category keyword dictionary and user (...) search records. The algorithm is mainly based on the self-built English oral category classification dictionary and converts the user’s query vector into a user-English-speaking type vector. Through the calculation rules proposed in this paper, the target user’s preference score for a specific type of spoken English is obtained, and this score is assigned to the unrated item of the original user’s feature matrix as the initial starting score. At the same time, in order to solve the problem of insufficient user similarity calculation accuracy, a user similarity calculation algorithm based on “Synonyms Cilin Extended Edition” and search records is proposed. The algorithm introduces “Synonyms Cilin” to calculate the correlation between the semantic items, vocabulary, and query vector in the user query record to obtain the similarity between users and finally gives a user similarity calculation that integrates user ratings and query vectors method. For the task of Chinese grammar error correction, this article uses two methods of predicting the relationship between words in the corpus, Word2Vec and GloVe, to train the word vectors of different dimensions and use the word vectors to represent the text features of the experimental samples, avoiding sentences brought by word segmentation. On the basis of word vectors, the advantages and disadvantages of CNN, LSTM, and SVM models in this shared task are analyzed through experimental data. The comparative experiment shows that the method in this paper has achieved relatively good results. (shrink)