Analyzing uninformed search strategy algorithms in state space search

Pooja, S; S, Chethan; Arjun, C

doi:10.1109/icgtspicc.2016.7955277

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Uninformed search approaches conduct a blind search of the search space without any prior knowledge of how close or far away the optimal answer is from the current solution [2]. Brute-force search, breadth-first search, depth-first search, and bidirectional search are examples of uninformed search algorithms [3,4]. Such search algorithms are comprehensive and infeasible for computationally expensive issues because they are time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

Khafaga¹,

El-kenawy²,

Karim³

et al. 2023

Computers, Materials &Amp; Continua

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Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning. Each feature in a dataset has 2 n possible subsets, making it challenging to select the optimum collection of features using typical methods. As a result, a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization (DTO-GW) algorithms has been developed in this research. Instability can result when the selection of features is subject to metaheuristics, which can lead to a wide range of results. Thus, we adopted hybrid optimization in our method of optimizing, which allowed us to better balance exploration and harvesting chores more equitably. We propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of attributes. In the proposed method, the number of features selected is minimized, while classification accuracy is increased. To test the proposed method's performance against eleven other state-of-theart approaches, eight datasets from the UCI repository were used, such as binary grey wolf search (bGWO), binary hybrid grey wolf, and particle swarm optimization (bGWO-PSO), bPSO, binary stochastic fractal search (bSFS), binary whale optimization algorithm (bWOA), binary modified grey wolf optimization (bMGWO), binary multiverse optimization (bMVO), binary bowerbird optimization (bSBO), binary hysteresis optimization (bHy), and binary hysteresis optimization (bHWO). The suggested method is superior 4532 CMC, 2023, vol.74, no.2 and successful in handling the problem of feature selection, according to the results of the experiments.

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Section: Introductionmentioning

confidence: 99%

Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

Khafaga¹,

El-kenawy²,

Karim³

et al. 2023

Computers, Materials &Amp; Continua

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“…At every point in the grid map representing the working area of the robot, the selected algorithm assigns a direction for the robot out of the possible four directions: Right, Left, Up, and Down. In this paper six different algorithms have been studied, categorized as Informed [11] and Uninformed types [12], a differentiation between them in characteristics is applied, how the algorithm itself works, how the steps of work are correctly applied, and finally, there were three different maps that graduate in their difficulty related to a number of obstacles [13] to test those algorithms, testing six different types of path planning algorithms with three different maps was a challenging work that has been done successfully.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Path Planning between Improved Informed and Uninformed Algorithms for Mobile Robot

Amr,

Bahgat,

Rashad

et al. 2022

IJACSA

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This work is concerned with the Path Planning Algorithms (PPA), which hold an important place in Robotics navigation. Navigation has become indispensable to most modern inventions. Mobile robots have to move to a relevant task point in order to achieve the tasks assigned to them. The actions, which are planned in a structure, may restrict the task duration and even in some situations, the mission tends to be accomplished. This paper aims to study and compare six commonly used informed and uninformed algorithms. Three different maps have been created with gradually increasing difficulty levels related to a number of obstacles in the tested maps. The paper provides a detailed comparison between the algorithms under investigation of several parameters such as: Total steps, straight steps, rotation steps, and search time. The promised results were obtained when the proposed algorithms were applied to a case study.

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Training through the implementation of a web-based laboratory for Artificial Intelligence exercises

Petrova

Atanasova

2019

2019 29th Annual Conference of the European Association for Education in Electrical and Information Engineering (EAEEIE)

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Analyzing uninformed search strategy algorithms in state space search

Cited by 3 publications

References 2 publications

Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

Comparison of Path Planning between Improved Informed and Uninformed Algorithms for Mobile Robot

Training through the implementation of a web-based laboratory for Artificial Intelligence exercises

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