scholarly journals Experimental Verification of the Hill-climbing Performance of Blade-Type Crawler for High-speed Rough-terrain

2017 ◽  
Vol 35 (2) ◽  
pp. 153-159
Author(s):  
Yasuyuki Yamada ◽  
Ryoichi Higashi ◽  
Gen Endo ◽  
Taro Nakamura
Keyword(s):  
Experimental Verification ◽  
High Speed ◽  
Hill Climbing ◽  
Rough Terrain ◽  
The Hill

An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem

2020 ◽  
Vol 16 (4) ◽  
pp. 296-306 ◽  
Author(s):  
Laith Mohammad Abualigah ◽  
Essam Said Hanandeh ◽  
Ahamad Tajudin Khader ◽  
Mohammed Abdallh Otair ◽  
Shishir Kumar Shandilya
Keyword(s):  
Optimization Technique ◽  
Document Clustering ◽  
Text Clustering ◽  
Hill Climbing ◽  
Text Documents ◽  
Clustering Problem ◽  
Text Document ◽  
Text Information ◽  
Amount Of Knowledge ◽  
The Hill

Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.

High-speed hazard avoidance for mobile robots in rough terrain

2004 ◽  
Author(s):  
Matthew J. Spenko ◽  
Karl D. Iagnemma ◽  
Steven Dubowsky
Keyword(s):  
Mobile Robots ◽  
High Speed ◽  
Rough Terrain ◽  
Hazard Avoidance

STAND-UP MOTION SUPPORT BY A MOBILE MANIPULATOR SYSTEM WITH HIGH SPEED TACTILE SENSORS

2011 ◽  
Vol 08 (03) ◽  
pp. 181-195
Author(s):  
ZHAOXIAN XIE ◽  
HISASHI YAMAGUCHI ◽  
MASAHITO TSUKANO ◽  
AIGUO MING ◽  
MAKOTO SHIMOJO
Keyword(s):  
Fuzzy Logic ◽  
Feedback Control ◽  
Experimental Verification ◽  
High Speed ◽  
Center Of Pressure ◽  
Tactile Sensor ◽  
Mobile Manipulator ◽  
Tactile Sensing ◽  
Robot Arm ◽  
Tactile Sensors

As one of the home services by a mobile manipulator system, we are aiming at the realization of the stand-up motion support for elderly people. This work is charaterized by the use of real-time feedback control based on the information from high speed tactile sensors for detecting the contact force as well as its center of pressure between the assisted human and the robot arm. First, this paper introduces the design of the tactile sensor as well as initial experimental results to show the feasibility of the proposed system. Moreover, several fundamental tactile sensing-based motion controllers necessary for the stand-up motion support and their experimental verification are presented. Finally, an assist trajectory generation method for the stand-up motion support by integrating fuzzy logic with tactile sensing is proposed and demonstrated experimentally.

scholarly journals Modified Average of the Base-Level Models in the Hill-Climbing Bagged Ensemble Selection Algorithm for Credit Scoring

2019 ◽  
Vol 157 ◽  
pp. 229-237
Author(s):  
Tri Handhika ◽  
Achmad Fahrurozi ◽  
Revaldo Ilfestra Metzi Zen ◽  
Dewi Putrie Lestari ◽  
Ilmiyati Sari ◽  
...  
Keyword(s):  
Credit Scoring ◽  
Hill Climbing ◽  
Selection Algorithm ◽  
Base Level ◽  
Ensemble Selection ◽  
The Hill

scholarly journals Adaptive fitness landscape for replicator systems: to maximize or not to maximize

2018 ◽  
Vol 13 (3) ◽  
pp. 25 ◽  
Author(s):  
Alexander S. Bratus ◽  
Yuri S. Semenov ◽  
Artem S. Novozhilov
Keyword(s):  
Natural Selection ◽  
Fitness Landscape ◽  
Evolutionary Process ◽  
Hill Climbing ◽  
Adaptive Landscape ◽  
Basic Model ◽  
Mean Fitness ◽  
The Mean ◽  
The Hill ◽  
Fisher’S Fundamental Theorem

Sewall Wright’s adaptive landscape metaphor penetrates a significant part of evolutionary thinking. Supplemented with Fisher’s fundamental theorem of natural selection and Kimura’s maximum principle, it provides a unifying and intuitive representation of the evolutionary process under the influence of natural selection as the hill climbing on the surface of mean population fitness. On the other hand, it is also well known that for many more or less realistic mathematical models this picture is a severe misrepresentation of what actually occurs. Therefore, we are faced with two questions. First, it is important to identify the cases in which adaptive landscape metaphor actually holds exactly in the models, that is, to identify the conditions under which system’s dynamics coincides with the process of searching for a (local) fitness maximum. Second, even if the mean fitness is not maximized in the process of evolution, it is still important to understand the structure of the mean fitness manifold and see the implications of this structure on the system’s dynamics. Using as a basic model the classical replicator equation, in this note we attempt to answer these two questions and illustrate our results with simple well studied systems.

Genetic Algorithm With Hill Climbing for Correspondences Discovery in Ontology Mapping

2019 ◽  
Vol 12 (4) ◽  
pp. 153-170 ◽  
Author(s):  
Guefrouchi Ryma ◽  
Kholladi Mohamed-Khireddine
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Ontology Mapping ◽  
Hill Climbing ◽  
Computational Time ◽  
Mutation Operator ◽  
Mapping Process ◽  
The Hill

Meta-heuristics are used as a tool for ontology mapping process in order to improve their performance in mapping quality and computational time. In this article, ontology mapping is resolved as an optimization problem. It aims at optimizing correspondences discovery between similar concepts of source and target ontologies. For better guiding and accelerating the concepts correspondences discovery, the article proposes a meta-heuristic hybridization which incorporates the Hill Climbing method within the mutation operator in the genetic algorithm. For test concerns, syntactic and lexical similarities are used to validate correspondences in candidate mappings. The obtained results show the effectiveness of the proposition for improving mapping performances in quality and computational time even for large OAEI ontologies.

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