Comparison of the number of nodes explored by cyclic best first search with depth contour and best first search

Zhang, Wenda; Sauppe, Jason J.; Jacobson, Sheldon H.

doi:10.1016/j.cor.2020.105129

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…BFS is one of the most used searchbased algorithms and is able to return lower bound feed-back of data values quickly, at the expense of wasteful reconstruction of discarded solutions [17]. In other words, BFS provide the output based on the lower bound value of the available solutions within the search nodes [42]. However, BFS suffers from memory complexity when dealing with large datasets.…”

Section: Best First Search (Bfs)mentioning

confidence: 99%

Enhancing Big Data Feature Selection Using a Hybrid Correlation-Based Feature Selection

et al. 2021

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This study proposes an alternate data extraction method that combines three well-known feature selection methods for handling large and problematic datasets: the correlation-based feature selection (CFS), best first search (BFS), and dominance-based rough set approach (DRSA) methods. This study aims to enhance the classifier’s performance in decision analysis by eliminating uncorrelated and inconsistent data values. The proposed method, named CFS-DRSA, comprises several phases executed in sequence, with the main phases incorporating two crucial feature extraction tasks. Data reduction is first, which implements a CFS method with a BFS algorithm. Secondly, a data selection process applies a DRSA to generate the optimized dataset. Therefore, this study aims to solve the computational time complexity and increase the classification accuracy. Several datasets with various characteristics and volumes were used in the experimental process to evaluate the proposed method’s credibility. The method’s performance was validated using standard evaluation measures and benchmarked with other established methods such as deep learning (DL). Overall, the proposed work proved that it could assist the classifier in returning a significant result, with an accuracy rate of 82.1% for the neural network (NN) classifier, compared to the support vector machine (SVM), which returned 66.5% and 49.96% for DL. The one-way analysis of variance (ANOVA) statistical result indicates that the proposed method is an alternative extraction tool for those with difficulties acquiring expensive big data analysis tools and those who are new to the data analysis field.

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Section: Best First Search (Bfs)mentioning

confidence: 99%

Enhancing Big Data Feature Selection Using a Hybrid Correlation-Based Feature Selection

et al. 2021

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“…where, AHt is the number of Add Head() operations, ATt is the number of Add Tail() operations and GHt is the number of Get Head() operations that is performed during trial t. Similar to (Zhang et al, 2021) and (Iordan, 2018), in this study, after the search algorithm has found a solution to solve the problem, the number of raised nodes Rt and the number of explored nodes Et are recorded. A ratio r of explored to raised node for each trial is computed as follows:…”

Section: Test Scenario and Performance Metricsmentioning

confidence: 99%

A Comparative Study of Informed and Uninformed Search Algorithm to Solve Eight-Puzzle Problem

Hidayat¹,

Susanti²,

Wijaya³

2021

Journal of Computer Science

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Problems in artificial intelligence can be solved using intelligent tracking methods through intelligent search mechanisms. Understandably, search algorithm performances are highly dependent on the problem solved. In this study, we evaluate and compare the performance of five uninformed and informed search (breadth-first search, depth first search, optimal search and best first search using two heuristic functions, namely mismatched tile and Manhattan distance) algorithms to solve the eight-puzzle game problem. For each algorithm, the numbers of raised and explored nodes were assessed and analyzed. Our experiment demonstrates that informed search with heuristic outperforms uninformed search significantly, both in terms of memory usage efficiency and computational power efficiency. On average, the informed search using heuristic requires only 5.33% of memory used by uninformed search and only 4.45% of computational power demanded by uninformed search. Boxplot analysis also confirms that informed search using heuristic also delivers more stable performance contrasted to uninformed search. These could be a concern for researchers and game developers to consider implementing the heuristically enhanced search algorithm to utilize memory and computational power efficiently to solve similar problems.

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An improved heuristic mechanism ant colony optimization algorithm for solving path planning

Liu¹,

Wu²,

Xiao³

et al. 2023

Knowledge-Based Systems

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Comparison of the number of nodes explored by cyclic best first search with depth contour and best first search

Cited by 4 publications

References 15 publications

Enhancing Big Data Feature Selection Using a Hybrid Correlation-Based Feature Selection

Enhancing Big Data Feature Selection Using a Hybrid Correlation-Based Feature Selection

A Comparative Study of Informed and Uninformed Search Algorithm to Solve Eight-Puzzle Problem

An improved heuristic mechanism ant colony optimization algorithm for solving path planning

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