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Detecting Promising Areas by Evolutionary Clustering Search
[chapter]
2004
Lecture Notes in Computer Science
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This paper proposes a way of detecting promising search areas based on clustering. ...
The search strategy becomes more aggressive in such detected areas by applying local search. ...
Evolutionary Clustering Search The Evolutionary Clustering Search (ECS) employs clustering for detecting promising areas of the search space. ...
doi:10.1007/978-3-540-28645-5_39
fatcat:mz3rnq7stjeinhckrsqf4jbgca
Hybrid Evolutionary Algorithms and Clustering Search
[chapter]
2007
Studies in Computational Intelligence
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In this chapter, the Clustering Search (*CS) is proposed as a generic way of combining search metaheuristics with clustering to detect promising search areas before applying local search procedures. ...
A challenge in hybrid evolutionary algorithms is to employ efficient strategies to cover all the search space, applying local search only in actually promising search areas. ...
This chapter proposes the Clustering Search (*CS): the generalized way of detecting promising search areas by clusters of solutions. ...
doi:10.1007/978-3-540-73297-6_4
fatcat:vxsumn5jgfa7nkl2bowrra4wni
Hybrid algorithms with detection of promising areas for the prize collecting travelling salesman problem
2005
Fifth International Conference on Hybrid Intelligent Systems (HIS'05)
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This paper approaches new heuristics to solve the PCTSP, using a hybrid evolutionary algorithm, called Evolutionary Clustering Search (ECS) and an adaptation of this, called * CS, where the evolutionary ...
component will be substituted by the metaheuristics GRASP and VNS. ...
Evolutionary Clustering Search The Evolutionary Clustering Search (ECS) is an evolutionary technique proposed by Oliveira and Lorena [14] that employs clustering to detect promising areas of the search ...
doi:10.1109/ichis.2005.57
dblp:conf/his/ChavesL05
fatcat:6g7wg7caunfste5l4s56jgwuxm
Pattern Sequencing Problems by Clustering Search
[chapter]
2006
Lecture Notes in Computer Science
Preserved Fulltext
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The file type is application/pdf.
This paper proposes the Clustering Search (*CS): a generic way of combining search metaheuristics with clustering to detect promising search areas before applying local search procedures. ...
A challenge in such algorithms is to discover efficient strategies to cover all the search space, applying local search only in actually promising search areas. ...
Clustering Search foundations The *CS employs clustering for detecting promising areas of the search space. ...
doi:10.1007/11874850_26
fatcat:2swo3hmv2jexfiijw4qv6wz3ne
Clustering Search Heuristic for the Capacitated p-Median Problem
[chapter]
2007
Advances in Soft Computing
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The purpose of this paper is to present a new hybrid heuristic to solve the CPMP, called Clustering Search (CS), which consists in detecting promising search areas based on clustering. ...
Clustering Search The Clustering Search (CS) generalizes the Evolutionary Clustering Search (ECS) proposed by Oliveira and Lorena [9] that employs clustering for detecting promising areas of the search ...
The CS attempts to locate promising search areas by framing them by clusters. ...
doi:10.1007/978-3-540-74972-1_19
dblp:series/asc/ChavesCL08
fatcat:7ss3zld7tbgn3cuzahoir53tu4
Hybrid Metaheuristic for the Prize Collecting Travelling Salesman Problem
[chapter]
2008
Lecture Notes in Computer Science
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This paper presents one solution procedure for the PCTSP, using a hybrid metaheuristic known as Clustering Search (CS), whose main idea is to identify promising areas of the search space by generating ...
solutions and clustering them into groups that are them explored further. ...
Clustering Search The Clustering Search (CS) generalizes the Evolutionary Clustering Search (ECS) proposed by Oliveira and Lorena [1] , that employ clustering for detecting promising areas of the search ...
doi:10.1007/978-3-540-78604-7_11
fatcat:n4j2ruhyvffthcaz5o42js746m
Clustering search
2013
Pesquisa Operacional
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Usually the local search is costly and should be used only in promising regions of the search space. The CS assists in the discovery of these regions by dividing the search space into clusters. ...
This paper presents the Clustering Search (CS) as a new hybrid metaheuristic, which works in conjunction with other metaheuristics, managing the implementation of local search algorithms for optimization ...
CS RELATED APPLICATIONS CS was early proposed as a hybrid evolutionary algorithm, aware of detecting promising search area based on clustering. ...
doi:10.1590/s0101-74382013000100007
fatcat:z6lofz3gpbeh7f72zdf7sl2c7a
An Evolutionary Approach towards Clustering Airborne Laser Scanning Data
[article]
2014
arXiv
pre-print
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The algorithm is compared to a traditional k-means clustering. ...
Further, by using euclidean distance as a metric, k-means searches for equally sized spheroids in all clustered dimensions. ...
The proposed algorithm is by no means the first to attempt clustering by using a genetic approach. ...
arXiv:1401.4848v1
fatcat:qxqyx5irvzaetkfv3dknhiawla
Evolutionary continuous dynamic optimization
2022
Proceedings of the Genetic and Evolutionary Computation Conference Companion
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Besides dynamically changing environments, his main research interests include evolutionary algorithms, large-scale optimization, simulation optimization, and their applications. ...
Convergence Detection Convergence detection by monitoring the fitness of the best found position by the subpopulation. ...
Population Division and Management
Population clustering approach By index: In this commonly used method, the individuals of each subpopulation are clustered according to their indices [1] . By ...
doi:10.1145/3520304.3533643
fatcat:ficzmwu2srhqdcgwec7wdrfhtu
Clustering search algorithm for the capacitated centered clustering problem
2010
Computers & Operations Research
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This paper presents a solution procedure for the CCCP, using the hybrid metaheuristic Clustering Search (CS), whose main idea is to identify promising areas of the search space by generating solutions ...
The Capacitated Centred Clustering Problem (CCCP) consists in partitioning a set of n points into p disjoint clusters with a known capacity. Each cluster is specified by a centroid. ...
Acknowledgments The authors acknowledge the useful comments and suggestions of an anonymous referee and the CNPq by a partial research support. ...
doi:10.1016/j.cor.2008.09.011
fatcat:rjwltyavsvcalkyhz5wzqukfsm
Survey on data science with population-based algorithms
2016
Big Data Analytics
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This paper discusses the relationship between data science and population-based algorithms, which include swarm intelligence and evolutionary algorithms. ...
Exploration means the ability of a search algorithm to explore different areas of the search space to have the high probability to find good promising solutions. ...
By cloning the best individual of successive generations, the CPSO algorithm could enlarge the area near the promising candidate solution and accelerate the evolution of solutions [49] . ...
doi:10.1186/s41044-016-0003-3
fatcat:jvddmsjmivdejehqdwlkm2csca
A clustering particle swarm optimizer for dynamic optimization
2009
2009 IEEE Congress on Evolutionary Computation
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In order to detect local optima as many as possible, it is important that how to guide particles searching in different promising regions. ...
A fast local search method is also proposed to find the near optimal solutions in a local promising region in the search space. ...
To overcome the above problems when using the multipopulation method, CPSO employs a global search method to detect promising sub-regions and a hierarchical clustering method to generate a proper number ...
doi:10.1109/cec.2009.4982979
dblp:conf/cec/LiY09a
fatcat:smt2smnwarepnir35372b5kzh4
Deep Learning based Approach for Bone Diagnosis Classification in Ultrasonic Computed Tomographic Images
2020
International Journal of Advanced Computer Science and Applications
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Artificial intelligence (AI) in the area of medical imaging has shown a developed technology to have automatically the true diagnosis especially in ultrasonic imaging area. ...
At second step, an evolutionary neural network is proposed with the AmeobaNet model for USCT image classification. ...
Its algorithm is illustrated by these steps [20] . • Fix the k number of cluster values. • Identify the k cluster centers. • Determine the cluster center. • Determine the pixel distance for each cluster ...
doi:10.14569/ijacsa.2020.0111210
fatcat:guz65s3ii5b3zib6o7yryvcnqa
A General Framework of Multipopulation Methods With Clustering in Undetectable Dynamic Environments
2012
IEEE Transactions on Evolutionary Computation
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search space throughout the run. ...
situation where changes can not be detected or predicted. ...
In FMSO, a parent swarm is used as a basic swarm to detect the most promising area when the environment changes, and a group of child swarms are used to search the local optimum in their own sub-spaces ...
doi:10.1109/tevc.2011.2169966
fatcat:62zsic63mvffzgvgfjhac3wy5i
A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization
[article]
2022
arXiv
pre-print
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Then, we synthetically overview recent advances of learnable MOEAs in solving various scaling up MOPs, focusing primarily on three attractive and promising directions (i.e., learnable evolutionary discriminators ...
the scaling-up MOPs with continuously increasing complexity or scale from diverse aspects, mainly including expensive function evaluations, many objectives, large-scale search space, time-varying environments ...
from intermediate solutions to guide their search away from mediocre solutions and toward potentially promising areas. ...
arXiv:2206.11526v1
fatcat:mlgcvsi4sfadflm3xxfkbfjk6q
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