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Detecting Promising Areas by Evolutionary Clustering Search [chapter]

Alexandre C. M. Oliveira, Luiz A. N. Lorena
2004 Lecture Notes in Computer Science  
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]

A. C. M. Oliveira, L. A. N. Lorena
2007 Studies in Computational Intelligence  
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

A.A. Chaves, L.A.N. Lorena
2005 Fifth International Conference on Hybrid Intelligent Systems (HIS'05)  
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]

Alexandre C. M. Oliveira, Luiz A. N. Lorena
2006 Lecture Notes in Computer Science  
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]

Antonio Augusto Chaves, Francisco Assis Correa, Luiz Antonio N. Lorena
2007 Advances in Soft Computing  
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]

Antonio Augusto Chaves, Luiz Antonio Nogueira Lorena
2008 Lecture Notes in Computer Science  
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

Alexandre César Muniz de Oliveira, Antonio Augusto Chaves, Luiz Antonio Nogueira Lorena
2013 Pesquisa Operacional  
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]

Ronald Hochreiter, Christoph Waldhauser
2014 arXiv   pre-print
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

Danial Yazdani, Xin Yao
2022 Proceedings of the Genetic and Evolutionary Computation Conference Companion  
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

Antonio Augusto Chaves, Luiz Antonio Nogueira Lorena
2010 Computers & Operations Research  
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

Shi Cheng, Bin Liu, T. O. Ting, Quande Qin, Yuhui Shi, Kaizhu Huang
2016 Big Data Analytics  
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

Changhe Li, Shengxiang Yang
2009 2009 IEEE Congress on Evolutionary Computation  
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

Marwa Fradi, Mouna Afif, Mohsen Machhout
2020 International Journal of Advanced Computer Science and Applications  
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

Changhe Li, Shengxiang Yang
2012 IEEE Transactions on Evolutionary Computation  
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]

Songbai Liu
2022 arXiv   pre-print
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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