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Feature Selection (FS) methods have been studied extensively in the literature, and there are a crucial component in machine learning techniques. However, unsupervised text feature selection has not been well studied in document clustering problems. Feature selection could be modelled as an optimization problem due to the large number of possible solutions that might be valid. In this paper, a memetic method that combines Differential Evolution (DE) with Simulated Annealing (SA) for unsupervised FS was proposed. Due to the use of only two values indicating the existence or absence of the feature, a binary version of differential evolution is used. A dichotomous DE was used for the purpose of the binary version, and the proposed method is named Dichotomous Differential Evolution Simulated Annealing (DDESA). This method uses dichotomous mutation instead of using the standard mutation DE to be more effective for binary purposes. The Mean Absolute Distance (MAD) filter was used as the feature subset internal evaluation measure in this paper. The proposed method was compared with other state-of-the-art methods including the standard DE combined with SA, which is named DESA in this paper, using five benchmark datasets. The F-micro, F-macro (F-scores) and Average Distance of Document to Cluster (ADDC) measures were utilized as the evaluation measures. The Reduction Rate (RR) was also used as an evaluation measure. Test results showed that the proposed DDESA outperformed the other tested methods in performing the unsupervised text feature selection.

This paper introduces a new memetic optimization algorithm called MeSA (Memetic Simulated Annealing) to address the data fitting problem with local-support free-form curves. The proposed method hybridizes simulated annealing with the COBYLA local search optimization method. This approach is further combined with the centripetal parameterization and the Bayesian information criterion to compute all free variables of the curve reconstruction problem with B-splines. The performance of our approach is evaluated by its application to four different shapes with local deformations and different degrees of noise and density of data points. The MeSA method has also been compared to the non-memetic version of SA. Our results show that MeSA is able to reconstruct the underlying shape of data even in the presence of noise and low density point clouds. It also outperforms SA for all the examples in this paper. This work has been supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grants TEC2013-47141-C4-R (RACHEL) and #TIN2012-30768 (Computer Science National Program) and Toho University (Funabashi, Japan).

Graph vertex coloring with a given number of colors is a well-known and much-studied NP-complete problem.The most effective methods to solve this problem are proved to be hybrid algorithms such as memetic algorithms or quantum annealing. Those hybrid algorithms use a powerful local search inside a population-based algorithm.This paper presents a new memetic algorithm based on one of the most effective algorithms: the Hybrid Evolutionary Algorithm HEA from Galinier and Hao (1999).The proposed algorithm, denoted HEAD - for HEA in Duet - works with a population of only two individuals.Moreover, a new way of managing diversity is brought by HEAD.These two main differences greatly improve the results, both in terms of solution quality and computational time.HEAD has produced several good results for the popular DIMACS benchmark graphs, such as 222-colorings for \textless{}dsjc1000.9\textgreater{}, 81-colorings for \textless{}flat1000\_76\_0\textgreater{} and even 47-colorings for \textless{}dsjc500.5\textgreater{} and 82-colorings for \textless{}dsjc1000.5\textgreater{}., Comment: 11 pages, 8 figures, 3 tables, 2 algorithms

To improve the search ability of biogeography-based optimization (BBO), this work proposed an improved biogeography-based optimization based on Affinity Propagation. We introduced the Memetic framework to the BBO algorithm, and used the simulated annealing algorithm as the local search strategy. MBBO enhanced the exploration with the Affinity Propagation strategy to improve the transfer operation of the BBO algorithm. In this work, the MBBO algorithm was applied to IEEE Congress on Evolutionary Computation (CEC) 2015 benchmarks optimization problems to conduct analytic comparison with the first three winners of the CEC 2015 competition. The results show that the MBBO algorithm enhances the exploration, exploitation, convergence speed and solution accuracy and can emerge as the best solution-providing algorithm among the competing algorithms.

E-Learning systems have proven to be fundamental in several areas of tertiary education and in business companies. There are many significant advantages for people who learn online such as convenience, portability, flexibility and costs. However, the remarkable velocity and volatility of modern knowledge due to the exponential growth of the World Wide Web, requires novel learning methods that offer additional features such as information structuring, efficiency, task relevance and personalization. This paper proposes a novel multi-agent e-Learning system empowered with (ontological) knowledge representation and memetic computing to efficiently manage complex and unstructured information that characterize e-Learning. In particular, differing from other similar approaches, our proposal uses 1) ontologies to provide a suitable method for modeling knowledge about learning content and activities, and 2) memetic agents as intelligent explorers in order to create in time and personalized e-Learning experiences that satisfy learners specific preferences. The proposed method has been tested by realizing a multi-agent software plug-in for an industrial e-Learning platform with experimentations to validate our memetic proposal in terms of flexibility, efficiency and interoperability. © 2010 IEEE.

Over recent years, there has been increasing interest of the research community towards evolutionary algorithms, i.e., algorithms that exploit computational models of natural processes to solve complex optimization problems. In spite of their ability to explore promising regions of the search space, they present two major drawbacks: 1) they can take a relatively long time to locate the exact optimum and 2) may sometimes not find the optimum with sufficient precision. Memetic Algorithms are evolutionary algorithms inspired by both Darwinian principles and Dawkins' notion of a meme, able not only to converge to high-quality solutions, but also search more efficiently than their conventional evolutionary counterparts. However, memetic approaches are affected by several design issues related to the different choices that can be made to implement them. This paper introduces a multiagent-based memetic algorithm which executes in a parallel way different cooperating optimization strategies in order to solve a given problem's instance in an efficient way. The algorithm adaptation is performed by jointly exploiting a knowledge extraction process together with a decision making framework based on fuzzy methodologies. The effectiveness of our approach is tested in several experiments in which our results are compared with those obtained by nonadaptive memetic algorithms. The superiority of the proposed strategy is manifest in the majority of cases. © 2011 IEEE.

Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering additional features as efficiency, task relevance and personalization. Computational Intelligence methodologies can support e-Learning system designers in two different aspects: (1) they represent the most suitable solution able to support learning content and activities, personalized to specific needs and influenced by specific preferences of the learner and (2) they assist designers with computationally efficient methods to develop "in time" e-Learning environments. This article attempts to achieve both results by exploiting an ontological representations of learning environment and memetic approach of optimization, integrated into a cooperative distributed problem solving framework. This synergy enables multi-island memetic approach managing a collection of models and processes for adapting an e-Learning system to the learner expectations and to formulate objectives in an effective and dynamic intelligent way. More precisely, our proposal exploits ontological representations of learning environment and a memetic distributed problem-solving approach to generate the best learning presentation and, at the same time, minimize the computational efforts necessary to compute optimal learning experiences. © 2011 Wiley Periodicals, Inc.

Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering additional features as efficiency, task relevance and personalization. Our proposal attempts to deal with these additional features by exploiting an ontological representations of learning environment and a memetic approach of optimization, integrated into a cooperative distributed problem solving framework. In detail, this paper describes a novel multi-island memetic approach managing a collection of models and processes for adapting an e-Learning system to the learner expectations and to formulate objectives in a effective and dynamic intelligent way. © 2008 IEEE.

e-Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering additional features as efficiency, task relevance and personalization. The main aim of adaptive eLearning is to support content and activities, personalized to specific needs and influenced by specific preferences of the learner. This paper describes a collection of models and processes for adapting an e-Learning system to the learner expectations and to formulate objectives in a dynamic intelligent way. Precisely, our proposal exploits ontological representations of learning environment and a memetic optimization algorithm capable of generating the best learning presentation in an efficient and qualitative way. © 2008 IEEE.