The Experts below are selected from a list of 10977 Experts worldwide ranked by ideXlab platform

Yuliang Lin - One of the best experts on this subject based on the ideXlab platform.

Mohammed Azmi Albetar - One of the best experts on this subject based on the ideXlab platform.

  • adaptive β Hill Climbing for optimization
    Soft Computing, 2019
    Co-Authors: Mohammed Azmi Albetar, Ibrahim Aljarah, Mohammed A Awadallah, Hossam Faris, Seyedali Mirjalili
    Abstract:

    In this paper, an adaptive version of $$\beta -$$ Hill Climbing is proposed. In the original $$\beta -$$ Hill Climbing, two control parameters are utilized to strike the right balance between a local-nearby exploitation and a global wide-range exploration during the search: $${\mathcal {N}}$$ and $$\beta $$ , respectively. Conventionally, these two parameters require an intensive study to find their suitable values. In order to yield an easy-to-use optimization method, this paper proposes an efficient adaptive strategy for these two parameters in a deterministic way. The proposed adaptive method is evaluated against 23 global optimization functions. The selectivity analysis to determine the optimal progressing values of $${\mathcal {N}}$$ and $$\beta $$ during the search is carried out. Furthermore, the behavior of the adaptive version is analyzed based on various problems with different complexity levels. For comparative evaluation, the adaptive version is initially compared with the original one as well as with other local search-based methods and other well-regarded methods using the same benchmark functions. Interestingly, the results produced are very competitive with the other methods. In a nutshell, the proposed adaptive $$\beta -$$ Hill Climbing is able to achieve the best results on 10 out of 23 test functions. For more validation, the test functions established in IEEE-CEC2015 are used with various scaling values. The comparative results show the viability of the proposed adaptive method.

  • a novel gene selection method using modified mrmr and hybrid bat inspired algorithm with β Hill Climbing
    Applied Intelligence, 2018
    Co-Authors: Osama Ahmad Alomari, Mohammed Azmi Albetar, Ahamad Tajudin Khader, Mohammed A Awadallah
    Abstract:

    This paper proposed a new gene selection method based on modified Minimum Redundancy Maximum Relevancy (MRMR) as a filtering approach and hybrid bat algorithm with β-Hill Climbing as an efficient wrapper approach. The gene selection is a process of selecting the discriminative genes that aid in the development of efficient cancer diagnosis and classification. In general, the current filter-based approaches produced gene subset according to its discriminative power. However, one of the deficiencies of single filter approaches is that it has high variability of the classification results. Accordingly, this study aim to improve MRMR through incorporating its with ensemble of filters to increase the robustness and the stability of MRMR. The result of filtering-based approach is a set of discriminative genes. The wrapper-based approach considers the results from the filtering-based approach to formulate the gene selection search space. In wrapper approach, bat algorithm is tailored for gene selection problem and hybridized with a powerful local search method called beta Hill Climbing to further stress the deep learning side in the search space navigation and thus find a very robust and stable discriminative genes. Bat-inspired algorithm (BA) is a recent swarm-based optimization method while β-Hill Climbing is an exploratory local search. The proposed method is called Robust MRMR and Hybrid Bat-inspired Algorithm (rMRMR-HBA). To evaluate the proposed method, ten well-known microarray datasets are experimented with. These datasets are varies in terms of number of genes, samples, and classes. For performance evaluation, the proposed filtering-based approach (i.e., rMRMR) is initially tested against the standard MRMR and other well-regard filtering approaches. Thereafter, the wrapper-based approach (i.e., HBA) is evaluated by studying the convergence behavior of BA with and without β-Hill Climbing. For comparative evaluation, the results of the proposed rMRMR-HBA were compared with state-of-art methods using the same microarray datasets. The comparative results show that our proposed approach achieved outstanding results in two out of ten datasets in terms of clarification accuracy and minimum number of genes.

  • hybridizing β Hill Climbing with wavelet transform for denoising ecg signals
    Information Sciences, 2018
    Co-Authors: Mohammed Azmi Albetar, Ahamad Tajudin Khader, Zaid Abdi Alkareem Alyasseri, Mohammed A Awadallah
    Abstract:

    Abstract This paper introduces βHCWT, a hybrid of the β-Hill Climbing metaheuristic algorithm and wavelet transform (WT), as a new method for denoising electrocardiogram (ECG) signals. ECG signals are non-stationary signals that provide a graphical measure of electrical activities in human heart muscles. However, given their non-stationarity, these signals frequently encounter noise and a low signal-to-noise ratio (SNR). The selection of wavelet parameters is a challenging task that is usually performed based on empirical evidence or experience. Therefore, in this paper, β-Hill Climbing is applied to find the optimal wavelet parameters that can obtain the minimum mean square error (MSE) between the original and denoised ECG signals. The proposed method was tested on a standard ECG dataset from MIT-BIH while its performance was evaluated by using percentage root mean square difference (PRD) and SNR as criteria. Meanwhile, the effect of β-Hill Climbing on the performance of WT was tested by comparing the proposed method with WT. The proposed method was then compared with the genetic algorithm in consideration of the performance of the WT parameters and adaptive thresholding methods. The proposed method demonstrated an outstanding performance in removing noise from non-stationary signals, and the quality of the output signal was deemed favorable for medical diagnosis.

  • beta β Hill Climbing an exploratory local search
    Neural Computing and Applications, 2017
    Co-Authors: Mohammed Azmi Albetar
    Abstract:

    Hill Climbing method is an optimization technique that is able to build a search trajectory in the search space until reaching the local optima. It only accepts the upHill movement which leads it to easily get stuck in local optima. Several extensions to Hill Climbing have been proposed to overcome such problem such as Simulated Annealing, Tabu Search. In this paper, an extension version of Hill Climbing method has been proposed and called $$\beta$$ -Hill Climbing. A stochastic operator called $$\beta$$ -operator is utilized in Hill Climbing to control the balance between the exploration and exploitation during the search. The proposed method has been evaluated using IEEE-CEC2005 global optimization functions. The results show that the proposed method is a very efficient enhancement to the Hill Climbing providing powerful results when it compares with other advanced methods using the same global optimization functions.

  • hybridizing cuckoo search algorithm with Hill Climbing for numerical optimization problems
    International Conference in Information Technology, 2017
    Co-Authors: Mohammad Shehab, Mohammed Azmi Albetar, Ahamad Tajudin Khader, Laith Mohammad Abualigah
    Abstract:

    The Cuckoo Search Algorithm (CSA) is a promising metaheuristic algorithm. It is applied to solve many problems in different fields. This paper proposes a new cuckoo search algorithm by combining the cuckoo search algorithm with the Hill Climbing method for solving the integer and minimax optimization problems. The proposed algorithm is named hybrid cuckoo search and Hill Climbing (CSAHC). CSAHC starts the search by applying the standard cuckoo search for the number of iterations then the best-obtained solution is passed to the Hill Climbing algorithm as an intensification process to accelerate the search and overcome the slow convergence of the standard cuckoo search algorithm. The proposed algorithm balances between the global exploration of the cuckoo search algorithm and the deep exploitation of the Hill Climbing method. The validation of the performance is determined by applying 13 benchmarks. The results of experimental simulations indicated that the CSAHC performs better than standard CSA.

Ioannis Tsamardinos - One of the best experts on this subject based on the ideXlab platform.

  • on scoring maximal ancestral graphs with the max min Hill Climbing algorithm
    International Journal of Approximate Reasoning, 2018
    Co-Authors: Vincenzo Lagani, Konstantinos Tsirlis, Sofia Triantafillou, Ioannis Tsamardinos
    Abstract:

    Abstract We consider the problem of causal structure learning in presence of latent confounders. We propose a hybrid method, MAG Max–Min Hill-Climbing (M3HC) that takes as input a data set of continuous variables, assumed to follow a multivariate Gaussian distribution, and outputs the best fitting maximal ancestral graph. M3HC builds upon a previously proposed method, namely GSMAG, by introducing a constraint-based first phase that greatly reduces the space of structures to investigate. On a large scale experimentation we show that the proposed algorithm greatly improves on GSMAG in all comparisons, and over a set of known networks from the literature it compares positively against FCI and cFCI as well as competitively against GFCI, three well known constraint-based approaches for causal-network reconstruction in presence of latent confounders.

  • The max-min Hill-Climbing Bayesian network structure learning algorithm
    Machine Learning, 2006
    Co-Authors: Ioannis Tsamardinos, Laura E. Brown, Constantin F. Aliferis
    Abstract:

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

Donald Yeung - One of the best experts on this subject based on the ideXlab platform.

  • Hill Climbing smt processor resource distribution
    ACM Transactions on Computer Systems, 2009
    Co-Authors: Seungryul Choi, Donald Yeung
    Abstract:

    The key to high performance in Simultaneous MultiThreaded (SMT) processors lies in optimizing the distribution of shared resources to active threads. Existing resource distribution techniques optimize performance only indirectly. They infer potential performance bottlenecks by observing indicators, like instruction occupancy or cache miss counts, and take actions to try to alleviate them. While the corrective actions are designed to improve performance, their actual performance impact is not known since end performance is never monitored. Consequently, potential performance gains are lost whenever the corrective actions do not effectively address the actual bottlenecks occurring in the pipeline.We propose a different approach to SMT resource distribution that optimizes end performance directly. Our approach observes the impact that resource distribution decisions have on performance at runtime, and feeds this information back to the resource distribution mechanisms to improve future decisions. By evaluating many different resource distributions, our approach tries to learn the best distribution over time. Because we perform learning online, learning time is crucial. We develop a Hill-Climbing algorithm that quickly learns the best distribution of resources by following the performance gradient within the resource distribution space. We also develop several ideal learning algorithms to enable deeper insights through limit studies.This article conducts an in-depth investigation of Hill-Climbing SMT resource distribution using a comprehensive suite of 63 multiprogrammed workloads. Our results show Hill-Climbing outperforms ICOUNT, FLUSH, and DCRA (three existing SMT techniques) by 11.4p, 11.5p, and 2.8p, respectively, under the weighted IPC metric. A limit study conducted using our ideal learning algorithms shows our approach can potentially outperform the same techniques by 19.2p, 18.0p, and 7.6p, respectively, thus demonstrating additional room exists for further improvement. Using our ideal algorithms, we also identify three bottlenecks that limit online learning speed: local maxima, phased behavior, and interepoch jitter. We define metrics to quantify these learning bottlenecks, and characterize the extent to which they occur in our workloads. Finally, we conduct a sensitivity study, and investigate several extensions to improve our Hill-Climbing technique.

  • learning based smt processor resource distribution via Hill Climbing
    International Symposium on Computer Architecture, 2006
    Co-Authors: Seungryul Choi, Donald Yeung
    Abstract:

    The key to high performance in Simultaneous Multithreaded (SMT) processors lies in optimizing the distribution of shared resources to active threads. Existing resource distribution techniques optimize performance only indirectly. They infer potential performance bottlenecks by observing indicators, like instruction occupancy or cache miss counts, and take actions to try to alleviate them. While the corrective actions are designed to improve performance, their actual performance impact is not known since end performance is never monitored. Consequently, potential performance gains are lost whenever the corrective actions do not effectively address the actual bottlenecks occurring in the pipeline. We propose a different approach to SMT resource distribution that optimizes end performance directly. Our approach observes the impact that resource distribution decisions have on performance at runtime, and feeds this information back to the resource distribution mechanisms to improve future decisions. By evaluating many different resource distributions, our approach tries to learn the best distribution over time. Because we perform learning on-line, learning time is crucial. We develop a Hill-Climbing algorithm that efficiently learns the best distribution of resources by following the performance gradient within the resource distribution space. This paper conducts an in-depth investigation of learningbased SMT resource distribution. First, we compare existing resource distribution techniques to an ideal learning-based technique that performs learning off-line. This limit study shows learning-based techniques can provide up to 19.2% gain over ICOUNT, 18.0% gain over FLUSH, and 7.6% gain over DCRA across 21 multithreaded workloads. Then, we present an on-line learning algorithm based on Hill-Climbing. Our evaluation shows Hill-Climbing provides a 12.4% gain over ICOUNT, 11.3% gain over FLUSH, and 2.4% gain over DCRA across a larger set of 42 multiprogrammed workloads.

Risa Risa Fathonah - One of the best experts on this subject based on the ideXlab platform.

  • pembuatan game slider puzzle menggunakan metode steepest ascent Hill Climbing berbasis android
    JURNAL ISTEK, 2015
    Co-Authors: Wisnu Uriawan, Adam Faroqi, Risa Risa Fathonah
    Abstract:

    Perkembangan game dari masa ke masa menjadi 9 generasi, dari console game hingga sampai saat ini merupakan generasi ke-9 yaitu era perkembangan game online dan game pada perangkat mobile yang menggunakan operating system Android. Seiring dengan berkembangnya game dan di dukung dengan perkembangan gadget yang kian pesat sesorang cenderung memilih untuk memanfaatkan perangkat mobile sebagai media untuk bermain game. Operating system pada smartphone, mobile phone berbasis android yang banyak digunakan terutama di Indonesia. Terdapat beberapa jenis permainan salah satunya permainan game puzzle-8 sebagai implementasi permainan logika. Terdapat beberapa metode yang digunakan dalam penyusunan game puzzle-8 yaitu metode steepest ascent Hill Climbing merupakan salah satu metode optimasi dengan menggunakan metode heuristic. Pada metode Steepest ascent Hill Climbing yang di implementasikan pada permainan puzzle proses pengujian dilakukan dengan menggunakan fungsi heuristic. Pembangkitan keadaan berikutnya sangat tergantung pada feedback dari prosedur pengujian. Dalam implementasi game ini dibangun menggunkan bahasa pemrograman java dan tools unity.

  • game slider puzzle menggunakan metode steepest ascent Hill Climbing basis android
    2014
    Co-Authors: Risa Risa Fathonah
    Abstract:

    Perkembangan game dari masa ke masa menjadi 9 generasi , dari game konsol hingga sampai saat ini merupakan generasi ke -9 yaitu era perkembangan game online dan game pada perangkat mobile.salah satunya adalah perangkat mobile yang menggunakan operating system Android. Seiring dengan berkembangnya game dan di dukung dengan perkembangan gadget yang kian pesat sesorang cenderung memilih untuk memanfaatkan perangkat mobile sebagai media untuk bermain game, dari sekian banyak operating system pada smartphone, mobile phone berbasis android yang banyak digunakan terutama di Indonesia. Terdapat beberapa jenis permainan salah satunya adalah permainan game puzzle -8 sebagai implementasi permainan logika. Terdapat beberapa metode yang digunakan dalam penyusunan game puzzle -8 salah satunya adalah metode steepest ascent Hill Climbing salah satu metode optimasi dengan menggunakan metode heuristic. Pada metode Steepest ascent Hill Climbing yang di implementasikan pada permainan puzzle proses pengujian dilakukan dengan menggunakan fungsi heuristic. Pembangkitan keadaan berikutnya sangat tergantung pada feedback dari prosedur pengetesan. Dalam implementasi game ini dibangun menggunkan bahasa pemrograman javascript dan tools Unity.