A comprehensive survey on cultural algorithms

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Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Document clustering with optimized unsupervised feature selection and centroid allocation

An effective document clustering system can significantly improve the tasks of document analysis, grouping, and retrieval. The performance of a document clustering system mainly depends on document preparation and allocation of cluster positions. As achieving optimal document clustering is a combinatorial NP-hard optimization problem, it becomes essential to utilize non-traditional methods to look for optimal or near-optimal solutions. During the allocation of cluster positions or the centroids allocation process, the extra text features that represent keywords in each document have an effect on the clustering results. A large number of features need to be reduced using dimensionality reduction techniques. Feature selection is an important step that can be used to reduce the redundant and inconsistent features. Due to a large number of the potential feature combinations, text feature selection is considered a complicated process. The persistent drawbacks of the current text feature selection methods such as local optima and absence of class labels of features were addressed in this thesis. The supervised and unsupervised feature selection methods were investigated. To address the problems of optimizing the supervised feature selection methods so as to improve document clustering, memetic hybridization between filter and wrapper feature selection, known as Memetic Algorithm Feature Selection, was presented first. In order to deal with the unlabelled features, unsupervised feature selection method was also proposed. The proposed unsupervised feature selection method integrates Simulated Annealing to the global search using Differential Evolution. This combination also aims to combine the advantages of both the wrapper and filter methods in a memetic scheme but on an unsupervised basis. Two versions of this hybridization were proposed. The first was named Differential Evolution Simulated Annealing, which uses the standard mutation of Differential Evolution, and the second was named Dichotomous Differential Evolution Simulated Annealing, which used the dichotomous mutation of the differential evolution. After feature selection two centroid allocation methods were proposed; the first is the combination of Chaotic Logistic Search and Discrete Differential Evolution global search, which was named Differential Evolution Memetic Clustering (DEMC) and the second was based on using the Gradient search using the k-means as a local search with a modified Differential Harmony global Search. The resulting method was named Memetic Differential Harmony Search (MDHS). In order to intensify the exploitation aspect of MDHS, a binomial crossover was used with it. Finally, the improved method is named Crossover Memetic Differential Harmony Search (CMDHS). The test results using the F-measure, Average Distance of Document to Cluster (ADDC) and the nonparametric statistical tests showed the superiority of the CMDHS over the baseline methods, namely the HS, DHS, k-means and the MDHS. The tests also show that CMDHS is better than the DEMC proposed earlier. Finally the proposed CMDHS was compared with two current state-of-the-art methods, namely a Krill Herd (KH) based centroid allocation method and an Artifice Bee Colony (ABC) based method, and found to outperform these two methods in most cases

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp