Distributed Adaptive Networks: A Graphical Evolutionary Game-Theoretic View

Distributed adaptive filtering has been considered as an effective approach for data processing and estimation over distributed networks. Most existing distributed adaptive filtering algorithms focus on designing different information diffusion rules, regardless of the nature evolutionary characteristic of a distributed network. In this paper, we study the adaptive network from the game theoretic perspective and formulate the distributed adaptive filtering problem as a graphical evolutionary game. With the proposed formulation, the nodes in the network are regarded as players and the local combiner of estimation information from different neighbors is regarded as different strategies selection. We show that this graphical evolutionary game framework is very general and can unify the existing adaptive network algorithms. Based on this framework, as examples, we further propose two error-aware adaptive filtering algorithms. Moreover, we use graphical evolutionary game theory to analyze the information diffusion process over the adaptive networks and evolutionarily stable strategy of the system. Finally, simulation results are shown to verify the effectiveness of our analysis and proposed methods.Comment: Accepted by IEEE Transactions on Signal Processin

Evolutionary Computation Applied to Adaptive Information Filtering

Information Filtering is concerned with filtering data streams in such a way as to leave only pertinent data (information) to be perused. When the data streams are produced in a changing environment the filtering has to adapt too in order to remain effective. Adaptive Information Filtering is concerned with filtering in changing environments. The changes may occur both on the transmission side (the nature of the streams can change), and on the reception side (the interest of a user can change). Weighted trigram analysis is a quick and flexible technique for describing the contents of a document. A novel application of evolutionary computation is its use in adaptive information filtering for optimizing various parameters, notably the weights associated with trigrams. The research described in this paper combines weighted trigram analysis, clustering, and a special two-pool evolutionary algorithm, to create an Adaptive Information Filtering system with such use ful properties as domain independence, spelling error insensitivity, adaptability, and optimal use of user feedback while minimizing the amount of user feedback required to function properly. We designed a special evolutionary algorithm with a two-pool strategy for this changing environment

Learning algorithms for adaptive digital filtering

In this thesis, we consider the problem of parameter optimisation in adaptive digital filtering. Adaptive digital filtering can be accomplished using both Finite Impulse Response (FIR) filters and Infinite Impulse Response Filters (IIR) filters. Adaptive FIR filtering algorithms are well established. However, the potential computational advantages of IIR filters has led to an increase in research on adaptive IIR filtering algorithms. These algorithms are studied in detail in this thesis and the limitations of current adaptive IIR filtering algorithms are identified. New approaches to adaptive IIR filtering using intelligent learning algorithms are proposed. These include Stochastic Learning Automata, Evolutionary Algorithms and Annealing Algorithms. Each of these techniques are used for the filtering problem and simulation results are presented showing the performance of the algorithms for adaptive IIR filtering. The relative merits and demerits of the different schemes are discussed. Two practical applications of adaptive IIR filtering are simulated and results of using the new adaptive strategies are presented. Other than the new approaches used, two new hybrid schemes are proposed based on concepts from genetic algorithms and annealing. It is shown with the help of simulation studies, that these hybrid schemes provide a superior performance to the exclusive use of any one scheme

Concepts of Adaptive Information Filtering

This paper was written for the project study “Adaptive Information Filtering” at the Department of Computer Science, Leiden University, The Netherlands. The assignment was to write an introduction to Adaptive Information Filtering (AIF), based on the author’s ideas for his M.Sc. thesis, and with as large an audience as possible in mind. In addition to a simple introduction to AIF, this paper should also provide easy introductions to clustering algorithms, evolutionary computation, and n-gram analysis. (Preface, page 2

Evolutionary Multi-objective Scheduling for Anti-Spam Filtering Throughput Optimization

This paper presents an evolutionary multi-objective optimization problem formulation for the anti-spam filtering problem, addressing both the classification quality criteria (False Positive and False Negative error rates) and email messages classification time (minimization). This approach is compared to single objective problem formulations found in the literature, and its advantages for decision support and flexible/adaptive anti-spam filtering configuration is demonstrated. A study is performed using the Wirebrush4SPAM framework anti-spam filtering and the SpamAssassin email dataset. The NSGA-II evolutionary multi-objective optimization algorithm was applied for the purpose of validating and demonstrating the adoption of this novel approach to the anti-spam filtering optimization problem, formulated from the multi-objective optimization perspective. The results obtained from the experiments demonstrated that this optimization strategy allows the decision maker (anti-spam filtering system administrator) to select among a set of optimal and flexible filter configuration alternatives with respect to classification quality and classification efficiency

Reference signal generator for active power filters using MGP-FIR filter designed by evolutionary programming

This paper describes a high-performance reference signal generator for active power filters extracting the fundamental signal component from distorted current signals. In order to achieve high-quality output as well as computationally effective algorithm, the generator employs an adaptive and predictive MGP-FIR (Multiplicative General Parameter) bandpass filter designed by evolutionary programming. Detailed procedures of MGP-FIR filtering and evolutionary optimization are first discussed; theoretical conclusions are verified by illustrative simulation results.reviewe

Tracking moving optima using Kalman-based predictions

The dynamic optimization problem concerns finding an optimum in a changing environment. In the field of evolutionary algorithms, this implies dealing with a timechanging fitness landscape. In this paper we compare different techniques for integrating motion information into an evolutionary algorithm, in the case it has to follow a time-changing optimum, under the assumption that the changes follow a nonrandom law. Such a law can be estimated in order to improve the optimum tracking capabilities of the algorithm. In particular, we will focus on first order dynamical laws to track moving objects. A vision-based tracking robotic application is used as testbed for experimental comparison

Beyond TREC's filtering track

Following the withdrawal of the filtering track from the latest TREC conferences, there is a niche for new evaluation standards. Towards this end, we suggest, based on variations of TREC's routing subtask, two new evaluation methodologies. The first can be used for evaluating single, multi-topic profiles and the second for testing the ability of a multi-topic profile to adapt to both modest variations and radical drifts in user interests

Mathematical control of complex systems 2013

Mathematical control of complex systems have already become an ideal research area for control engineers, mathematicians, computer scientists, and biologists to understand, manage, analyze, and interpret functional information/dynamical behaviours from real-world complex dynamical systems, such as communication systems, process control, environmental systems, intelligent manufacturing systems, transportation systems, and structural systems. This special issue aims to bring together the latest/innovative knowledge and advances in mathematics for handling complex systems. Topics include, but are not limited to the following: control systems theory (behavioural systems, networked control systems, delay systems, distributed systems, infinite-dimensional systems, and positive systems); networked control (channel capacity constraints, control over communication networks, distributed filtering and control, information theory and control, and sensor networks); and stochastic systems (nonlinear filtering, nonparametric methods, particle filtering, partial identification, stochastic control, stochastic realization, system identification)