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Memetic Algorithms to Product-Unit Neural Networks for Regression
[chapter]
2005
Lecture Notes in Computer Science
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In this paper we present a new method for hybrid evolutionary algorithms where only a few best individuals are subject to local optimization. ...
The key aspect of our work is the use of a clustering algorithm to select the individuals to be optimized. ...
Hybrid Evolutionary Programming Algorithms We propose two methods of hybrid evolutionary algorithms based on the use of a clustering algorithm for deciding which individuals are subject to local optimization ...
doi:10.1007/11494669_11
fatcat:3rilpii62vetrnpqwnfpanbgci
A Novel Immune Optimization with Shuffled Frog Leaping Algorithm - A Parallel Approach for Unsupervised Data Clustering
2016
International Journal of Computer Applications
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This hybrid algorithm is developed by utilizing the benefits of both social and immune mechanisms. ...
Data clustering is one of the data mining task, it is used to group the data objects according to their similarity. ...
The entire working of this Hybrid Evolutionary algorithm can be shown in Fig.1 . ...
doi:10.5120/ijca2016909423
fatcat:wmeflcbfffcwbc742pi5dp4biu
Estimation of Evolutionary Optimization Algorithm for Association Rule using Spatial Data Mining
2012
International Journal of Computer Applications
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This research paper present a novel hybrid evolutionary algorithm (HEA) [2] which uses particle swarm optimization for spatial association rule mining with clustering. ...
To optimize the rules generated by Association Rule Mining (Apriori method) [1] use hybrid evolutionary algorithm. ...
The main area of concentration in this paper is to optimize the rules generated by Association Rule Mining (Apriori method) [1] [5] , using hybrid evolutionary algorithm. ...
doi:10.5120/8019-8204
fatcat:tir6kxj7xbhzpdcsalwh326o6y
2010 Index IEEE Transactions on Evolutionary Computation Vol. 14
2010
IEEE Transactions on Evolutionary Computation
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., +, TEVC Dec. 2010 842-864 Toward an Estimation of Nadir Objective Vector Using a Hybrid of Evolutionary and Local Search Approaches. ...
., +, TEVC Oct. 2010 801-818 Toward an Estimation of Nadir Objective Vector Using a Hybrid of Evolutionary and Local Search Approaches. ...
doi:10.1109/tevc.2010.2097050
fatcat:qhxnf2o62bd3xmxsfyd5fb37ym
An Improved PSO-GA Hybrid Algorithm Based on P Systems for Data Clustering
2017
International Journal of Multimedia and Ubiquitous Engineering
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But its random choice of initial cluster centers may lead to trapping into the local optimum and attaining unstable clustering results. ...
For this problem, P systems is introduced into a modified evolutionary algorithm which combines particle swarm optimization with an improved genetics algorithm (PGHAPS) in this paper. ...
This hybrid evolutionary algorithm is then used to improve the K-means by optimizing the initial cluster centers. ...
doi:10.14257/ijmue.2017.12.6.05
fatcat:no7rtadm3jhk5fexhwpx5a3tdm
Efficiently Finding the Optimum Number of Clusters in a Dataset with a New Hybrid Cellular Evolutionary Algorithm
2014
Journal of Computacion y Sistemas
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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; on the other hand, clustering ...
In this article, a new cellular evolutionary algorithm based on a hybrid model of global and local heuristic search is proposed for the same task, and extensive experimentation is done with different datasets ...
Acknowledgements The authors would like to express their gratitude to SIP-IPN, CONACyT and ICyT-DF for their economic support of this research, particularly, through grants SIP-20130932 and ICyT-PICCO- ...
doi:10.13053/cys-18-2-1936
fatcat:thvzft5slrfgzbqolvrt6ik5zi
Efficiently Finding the Optimum Number of Clusters in a Dataset with a New Hybrid Cellular Evolutionary Algorithm
2014
Journal of Computacion y Sistemas
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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; on the other hand, clustering ...
In this article, a new cellular evolutionary algorithm based on a hybrid model of global and local heuristic search is proposed for the same task, and extensive experimentation is done with different datasets ...
Acknowledgements The authors would like to express their gratitude to SIP-IPN, CONACyT and ICyT-DF for their economic support of this research, particularly, through grants SIP-20130932 and ICyT-PICCO- ...
doi:10.13053/cys-18-1-2014-034
fatcat:sfypjdjjeja5feakcxeyeftfym
Efficiently Finding the Optimum Number of Clusters in a Dataset with a New Hybrid Cellular Evolutionary Algorithm
2014
Journal of Computacion y Sistemas
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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; on the other hand, clustering ...
In this article, a new cellular evolutionary algorithm based on a hybrid model of global and local heuristic search is proposed for the same task, and extensive experimentation is done with different datasets ...
Acknowledgements The authors would like to express their gratitude to SIP-IPN, CONACyT and ICyT-DF for their economic support of this research, particularly, through grants SIP-20130932 and ICyT-PICCO- ...
doi:10.13053/cys-18-2-2014-034
fatcat:ezybllp6pvdvvdtgagqtmaz65a
BPSO Optimized K-means Clustering Approach for Data Analysis
2016
International Journal of Computer Applications
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Two evolutionary optimization algorithms BFO and PSO are combined to optimize KM algorithm to guarantee that the result of clustering is more accurate than clustering by basic KM algorithm. ...
In this dissertation, KM clustering problem is solved by optimized KM. ...
PROPOSED WORK 4.1 Problem Description In this work a hybrid evolutionary optimization technique for data clustering is used which used KM objective function for data clustering. ...
doi:10.5120/ijca2016907945
fatcat:lnzwabwmmzhsxm3eqbto5eunru
Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews
[chapter]
2007
Studies in Computational Intelligence
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All these clearly illustrates the need for hybrid evolutionary approaches where the main task is to optimize the performance of the direct evolutionary approach. ...
In this chapter, first we emphasize the need for hybrid evolutionary algorithms and then we illustrate the various possibilities for hybridization of an evolutionary algorithm and also present some of ...
The proposed approach selects a subset of the best individuals, perform a cluster analysis to group them, and optimize only the best individual of every group. ...
doi:10.1007/978-3-540-73297-6_1
fatcat:u64nkslegbhatfwt3dvzlfsifi
Distance Based Hybrid Approach for Cluster Analysis Using Variants of K-means and Evolutionary Algorithm
2014
Research Journal of Applied Sciences Engineering and Technology
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In recent years, Evolutionary algorithms are the global optimization techniques for solving clustering problems. ...
However, they are sensitive to random selection of initial centroids and are fall into local optimal solution. K-means++ algorithm has good convergence rate than other algorithms. ...
However, they are easily struck at local optimal solution and are sensitive to random selection of initial centers. The number of clusters also must be known in advance. ...
doi:10.19026/rjaset.8.1107
fatcat:cwxyszcyofcixl3nfschzsseva
Hybridization of evolutionary algorithms and local search by means of a clustering method
2005
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
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In the methodology presented, only a few individuals are subject to local optimization. Moreover, the local optimization algorithm is only applied at specific stages of the evolutionary process. ...
This paper proposes the combination of an EA, a clustering process, and a local-search procedure to the evolutionary design of product-units neural networks. ...
García On the other hand, clustering methods are a class of global optimization methods of which an important part includes a cluster-analysis technique. ...
doi:10.1109/tsmcb.2005.860138
pmid:16761808
fatcat:aa6oxelgbjdx5mpzhcjya2iexq
A Hybrid Evolutionary Algorithm Based on ACO and SA for Cluster Analysis
2008
Journal of Applied Sciences
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This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Ant Colony Optimization (ACO) and Simulated Annealing (SA), called ACO-SA, for cluster analysis. ...
Clustering problems appear in a wide range of unsupervised classification applications such as pattern recognition, vector quantization, data mining and knowledge discovery. ...
CONCLUSION A hybrid evolutionary optimization algorithm to solve clustering problems has been developed in this paper. ...
doi:10.3923/jas.2008.2695.2702
fatcat:5qvdm7b7prgkxcrxqjvpcdmaki
Admixture of hybrid swarms of native and introduced lizards in cities is determined by the cityscape structure and invasion history
2018
Proceedings of the Royal Society of London. Biological Sciences
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In cities with non-native lineages, lizards commonly occurred in numerous clusters of hybrid swarms, which showed variable lineage composition, consisting of up to four distinct evolutionary lineages. ...
Cityscape structure and invasion histories of cities will determine future evolutionary pathways at these novel hybrid zones. ...
We also owe thanks to William Peterman for valuable suggestions on his R package ResistanceGA and the NABU Mannheim for making available data on local lizard distribution. ...
doi:10.1098/rspb.2018.0143
pmid:30051861
fatcat:bfsqfran4valllxel4s5mcsmty
A New Evolutionary Algorithm For Cluster Analysis
2008
Zenodo
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In order to overcome K-means algorithm shortcomings, this paper proposes a hybrid evolutionary algorithm based on the combination of PSO, SA and K-means algorithms, called PSO-SA-K, which can find better ...
Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the kmeans algorithm. ...
Also in order to the Kmeans shortcomings a hybrid algorithm is developed based on combination of proposed PSO-SA and K-means algorithms for optimally clustering N object into K clusters. ...
doi:10.5281/zenodo.1329264
fatcat:r3gfwbsuzvehvo6n7kko2447hu
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