Memetic Computing

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Extreme learning machine via free sparse transfer representation optimization
Memetic Computing - Tập 8 - Trang 85-95 - 2016
Xiaodong Li, Weijie Mao, Wei Jiang, Ye Yao
In this paper, we propose a general framework for Extreme Learning Machine via free sparse transfer representation, which is referred to as transfer free sparse representation based on extreme learning machine (TFSR-ELM). This framework is suitable for different assumptions related to the divergence measures of the data distributions, such as a maximum mean discrepancy and K-L divergence. We propo...... hiện toàn bộ
A benchmark for cooperative coevolution
Memetic Computing - Tập 4 - Trang 263-277 - 2012
Alberto Tonda, Evelyne Lutton, Giovanni Squillero
Cooperative co-evolution algorithms (CCEA) are a thriving sub-field of evolutionary computation. This class of algorithms makes it possible to exploit more efficiently the artificial Darwinist scheme, as soon as an optimisation problem can be turned into a co-evolution of interdependent sub-parts of the searched solution. Testing the efficiency of new CCEA concepts, however, it is not straightforw...... hiện toàn bộ
Thematic issue on hybrid nature-inspired algorithms: concepts, analysis and applications
Memetic Computing - Tập 7 - Trang 1-2 - 2015
Fast distant support vector data description
Memetic Computing - Tập 9 - Trang 3-14 - 2016
Ping Ling, Xiangyang You, Dajin Gao, Tao Gao, Xue Li
As an indispensable approach of one class classification, support vector data description (SVDD) has been studied within diverse research areas and application domains. Distant SVDD (dSVDD) is a variant of SVDD that shows higher identification accuracy. However, dSVDD is caught by the pricy cost and troublesome parameterization, which diminishes its popularity. This paper proposes a fast distant S...... hiện toàn bộ
Where is the brain inside the brain?
Memetic Computing - Tập 3 - Trang 217-228 - 2011
Julian F. Miller, Gul Muhammad Khan
Biological brains are capable of general learning without supervision. This is learning across multiple domains without interference. Unlike artificial neural networks, in real brains, learned information is not purely encoded in real-valued weights but instead it resides in many neural aspects. Such aspects include, dendritic and axonal morphology, number and location of synapses, synaptic streng...... hiện toàn bộ
Guest Editorial: Thematic Issue on “Memetic Algorithms: Theory and applications in OR/MS”
Memetic Computing - Tập 4 - Trang 87-88 - 2012
Enrique Alba, Mauricio G. C. Resende, María E. Urquhart, Meng-Hiot Lim
A decomposition-based many-objective evolutionary algorithm with weight grouping and adaptive adjustment
Memetic Computing - - Trang 1-23 - 2023
Xiaoxin Gao, Fazhi He, Jinkun Luo, Tongzhen Si
Multiobjective evolutionary algorithms based on decomposition (MOEA/D) have attracted tremendous interest and have been thoroughly developed because of their excellent performance in multi/many-objective optimization problems. In general, MOEA/D methods use a set of uniformly distributed weight vectors to decompose a multiobjective problem into multiple single-objective subproblems and solve them ...... hiện toàn bộ
On principle axis based line symmetry clustering techniques
Memetic Computing - Tập 3 - Trang 129-144 - 2010
Sriparna Saha, Sanghamitra Bandyopadhyay
In this paper, at first a new line symmetry (LS) based distance is proposed which calculates the amount of symmetry of a point with respect to the first principal axis of a data set. The proposed distance uses a recently developed point symmetry (PS) based distance in its computation. Kd-tree based nearest neighbor search is used to reduce the complexity of computing the closest symmetric point. T...... hiện toàn bộ
Convex hull-based multi-objective evolutionary computation for maximizing receiver operating characteristics performance
Memetic Computing - Tập 8 - Trang 35-44 - 2015
Wenjing Hong, Ke Tang
The receiver operating characteristics (ROC) analysis has gained increasing popularity for analyzing the performance of classifiers. In particular, maximizing the convex hull of a set of classifiers in the ROC space, namely ROCCH maximization, is becoming an increasingly important problem. In this work, a new convex hull-based evolutionary multi-objective algorithm named ETriCM is proposed for evo...... hiện toàn bộ
Selecting survivors in genetic algorithm using tabu search strategies
Memetic Computing - Tập 1 - Trang 191-203 - 2009
Chuan-Kang Ting, Cheng-Feng Ko, Chih-Hui Huang
Genetic algorithm (GA) is well-known for its effectiveness in global search and optimization. To balance selection pressure and population diversity is an important issue of designing GA. This paper proposes a novel hybridization of GA and tabu search (TS) to address this issue. The proposed method embeds the key elements of TS—tabu restriction and aspiration criterion—into the survival selection ...... hiện toàn bộ
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