Assumption 0 analysis: comparative phylogenetic studies in the age of complexity

Darwin's panoramic view of biology encompassed two metaphors: the phylogenetic tree, pointing to relatively linear (and divergent) complexity, and the tangled bank, pointing to reticulated (and convergent) complexity. The emergence of phylogenetic systematics half a century ago made it possible to investigate linear complexity in biology. Assumption 0, first proposed in 1986, is not needed for cases of simple evolutionary patterns, but must be invoked when there are complex evolutionary patterns whose hallmark is reticulated relationships. A corollary of Assumption 0, the duplication convention, was proposed in 1990, permitting standard phylogenetic systematic ontology to be used in discovering reticulated evolutionary histories. In 2004, a new algorithm, phylogenetic analysis for comparing trees (PACT), was developed specifically for use in analyses invoking Assumption 0. PACT can help discern complex evolutionary explanations for historical biogeographical, coevolutionary, phylogenetic, and tokogenetic processe

Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History

A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on K-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH) is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional), 10 CEC2005 benchmark functions (30-dimensional), and a real-world problem (multilevel image segmentation problems). Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests

A Communication Framework Towards Flexible Associations of Business Entities Within Evolving Environments

The Internet and its manifestations, such as electronic commerce or in general network communication between different groups of interest (i.e., agents) have become indispensable for many of us. To adequately use the ever increasing amount of data, attempts are being made to extend data processing from a merely lexical view towards more complex, but equally important, multi-level view, including meaning and/or context (e.g., DAML, Web Services). The goal of this paper is to introduce a formal framework, apt to model communications from such a multi-level perspective. Therein, we discuss fundamental ideas of communication, such as agents involved and their respective structure. We integrate the concept of an agent's adaptive behaviour in order to assure a high degree of understanding. The framework is then illustrated using practical examples where we briefly present its usefulness and how it may be further developed. L'Internet et l'utilisation qu'on en fait, par exemple le commerce électronique ou plus généralement l'établissement de réseaux de communications entre différents intervenants (c.-à-d., agents) est devenu indispensable pour plusieurs d'entre nous. Il devient de plus en plus difficile d'utiliser adéquatement la vaste quantité de données s'y trouvant. À cette fin, de nombreuses initiatives tentent de faire évoluer les systèmes d'information les faisant passer de simples outils permettant le traitement lexical des données à des engins complexes comprenant les données et leur contexte d'interprétation (p.ex., DAML, Web Services). Dans cet article, nous présentons un cadre formel qui modélise les interactions, tout en tenant compte de plusieurs niveaux d'abstraction (p.ex., lexical, syntaxique, sémantique, etc.). Nous nous attardons aux concepts fondamentaux de la communication, tels que les agents impliqués dans les interactions et leur structure. Nous considérons aussi comment ces agents évoluent pour assurer la plus grande compréhension possible des messages reçus. Des exemples concrets servent à mieux expliquer comment le cadre peut être utilisé et comment il peut être raffiné.Inter-enterprise communication framework, information system evolution, adaptive systems., Cadre descriptif des communications inter-entreprise, évolution des systèmes d'information, systèmes adaptatifs

Knowledge management overview of feature selection problem in high-dimensional financial data: Cooperative co-evolution and Map Reduce perspectives

The term big data characterizes the massive amounts of data generation by the advanced technologies in different domains using 4Vs volume, velocity, variety, and veracity-to indicate the amount of data that can only be processed via computationally intensive analysis, the speed of their creation, the different types of data, and their accuracy. High-dimensional financial data, such as time-series and space-Time data, contain a large number of features (variables) while having a small number of samples, which are used to measure various real-Time business situations for financial organizations. Such datasets are normally noisy, and complex correlations may exist between their features, and many domains, including financial, lack the al analytic tools to mine the data for knowledge discovery because of the high-dimensionality. Feature selection is an optimization problem to find a minimal subset of relevant features that maximizes the classification accuracy and reduces the computations. Traditional statistical-based feature selection approaches are not adequate to deal with the curse of dimensionality associated with big data. Cooperative co-evolution, a meta-heuristic algorithm and a divide-And-conquer approach, decomposes high-dimensional problems into smaller sub-problems. Further, MapReduce, a programming model, offers a ready-To-use distributed, scalable, and fault-Tolerant infrastructure for parallelizing the developed algorithm. This article presents a knowledge management overview of evolutionary feature selection approaches, state-of-The-Art cooperative co-evolution and MapReduce-based feature selection techniques, and future research directions