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

Greedy Population Sizing for Evolutionary Algorithms

The number of parameters that need to be man ually tuned to achieve good performance of Evolutionary Algorithms and the dependency of the parameters on each other make this potentially robust and efficient computational method very time consuming and difficult to use. This paper introduces a Greedy Population Sizing method for Evolutionary Algo rithms (GPS-EA), an automated population size tuning method that does not require any population size related parameters to be specified or manually tuned a priori. Theoretical analysis of the number of function evaluations needed by the GPS EA to produce good solutions is provided. We also perform an empirical comparison of the performance of the GPS-EA to the performance of an EA with a manually tuned fixed population size. Both theoretical and empirical results show that using GPS-EA eliminates the need for manually tuning the population size parameter, while finding good solutions. This comes at the price of using twice as many function evaluations as needed by the EA with an optimal fixed population size; this, in practice, is a low price considering the amount of time and effort it takes to find this optimal population size manually

Toward Automating EA Configuration: The Parent Selection Stage

One of the obstacles to Evolutionary Algorithms (EAs) fulfilling their promise as easy to use general-purpose problem solvers, is the difficulty of correctly configuring them for specific problems such as to obtain satisfactory performance. Having a mechanism for automatically configuring parameters and operators of every stage of the evolutionary life-cycle would give EAs a more widely spread popularity in the non-expert community. This paper investigates automatic configuration of one of the stages of the evolutionary life-cycle, the parent selection, via a new concept of semi-autonomous parent selection, where mate selection operators are encoded and evolved as in Genetic Programming. We compare the performance of the EA with semi-autonomous parent selection to that of a manually configured EA on three common test problems to determine the “price” we pay for user-friendliness

Adaptive Resonance Theory (ART): An Introduction

this paper is to provide an introduction to Adaptive Resonance Theory (ART) by examining ART-1, the first member of the family of ART neural networks. The only prerequisite knowledge in the area of neural networks necessary for understanding this paper is backpropagation [Hinton86]. For an easy introduction to neural networks see [Freeman91], for a more in depth overview of the field see [Hertz91]. Many interesting problems concern the classification of data. For example, say we want to classify animals according to certain characteristics described by a set of parameters. We might have a dog, a cat and an owl. Some characteristics might be "number of legs", "can fly", "has fur" and "is a carnivore". With these characteristics we would hope that the cat and the dog are classified together and the owl separately. In this paper an algorithm which performs this mapping is called a clustering algorithm. A clustering algorithm takes as input a set of input vectors and gives as output a set of clusters and a mapping of each input vector to a cluster. Input vectors which are close to each other according to a specific similarity measure should be mapped to the same cluster. Clusters can be labelled to indicate a particular semantic meaning pertaining to all input vectors mapped to that cluster. The cat and the dog might be classified in a cluster labelled "mammals" and the owl in "birds". However one could also choose "pets" as label for the cluster with the cat and the dog and "winged animal" for the other. Clusters are usually internally represented using prototype vectors which are vectors indicating a certain similarity between the input vectors which are mapped to a cluster. In the above example the first cluster might have prototype vector (4 legs,can't fly,has fur,is a ..

The Automated Design of Probabilistic Selection Methods for Evolutionary Algorithms

Selection functions enable Evolutionary Algorithms (EAs) to apply selection pressure to a population of individuals, by regulating the probability that an individual\u27s genes survive, typically based on fitness. Various conventional fitness based selection methods exist, each providing a unique relationship between the fitnesses of individuals in a population and their chances of selection. However, the full space of selection algorithms is only limited by max algorithm size, and each possible selection algorithm is optimal for some EA configuration applied to a particular problem class. Therefore, improved performance may be expected by tuning an EA\u27s selection algorithm to the problem at hand, rather than employing a conventional selection method. The objective of this paper is to investigate the extent to which performance can be improved by tuning selection algorithms, employing a Hyper-heuristic to explore the space of search algorithms which encode the relationships between the fitnesses of individuals and their probability of selection. We show the improved performance obtained versus conventional selection functions on fixed instances from a benchmark problem class, including separate testing instances to show generalization of the improved performance

Infrastructure Hardening: A Competitive Co-evolutionary Methodology Inspired by Neo-Darwinian Arms Races

The world is increasingly dependent on critical infrastructures such as the electric power grid, water, gas, and oil transport systems, which are susceptible to cascading failures that can result from a few faults. Due to the combinatorial complexity in the search spaces involved, most traditional search techniques are inappropriate for identifying these faults and potential protections against them. This paper provides a computational methodology employing competitive coevolution to simultaneously identify low-effort, high-impact faults and corresponding means of hardening infrastructures against them. A power system case study provides empirical evidence that our proposed methodology is capable of identifying cost effective modifications to substantially improve the fault tolerance of critical infrastructures

Optimal Placement and Control of Unified Power Flow Control Devices Using Evolutionary Computing and Sequential Quadratic Programming

A crucial factor effecting modern power systems today is power flow control. An effective means for controlling and improving power flow is by installing fast reacting devices such as a unified power flow controller (UPFC). For maximum positive impact of this device on the power grid, it should be installed at an optimal location and employ an optimal realtime control algorithm. This paper proposes the combination of an evolutionary algorithm (EA) to find the optimal location and sequential quadratic programming (SQP) to optimize the UPFC control settings. Simulations are conducted using the classic IEEE 118 bus test system. For comparison purposes, results for the combination of a greedy placement heuristic (H) and the SQP control algorithm are provided as well. The EA+SQP combination is shown to outperform the H+SQP approach

Blueprint for Iteratively Hardening Power Grids Employing Unified Power Flow Controllers

A stable electricity supply is vital for modern society. However, many parts of our power transmission grid are operating near their operational limits. Such stressed systems are vulnerable to cascading failures, where a few small faults can induce a cascade of failures potentially leading to a major blackout The unified power flow controller (UPFC), the most powerful highspeed, semi-conductor based power flow device, can be used as a theoretical model to study how these devices can be used to improve power grid resilience. The blueprint presented here can be used to iteratively identify critical weaknesses in power grids and to recommend a means of fixing these weaknesses via the installation of UPFCs. This approach to hardening the power transmission grid will make it less prone to blackouts and better able to forestall or reduce the severity of unavoidable blackouts

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

UPFC Control Employing Gradient Descent Search

Increasing demand coupled with limitations on new construction indicate that existing power transmission must be better controlled in order to continue reliable operation. Recent advances in FACTS devices provide a mechanism to better control power flow on the transmission network. One particular device, the unified power flow controller (UPFC), holds the most promise for maintaining operation even when the system has suffered partial failure (either naturally occurring, due to human error, or a malicious attack). In addition to the capital cost, the primary obstacles to widespread UPFC use are the combined problems of selecting the most cost effective locations for installation and maintaining proper control of them once installed. In this paper we list evidence that gradient descent search based on load-flow computation is more realistic and accurate than many of the optimization techniques currently in use. We then demonstrate that gradient descent search can be used to select control points that improve system fault tolerance more than those found by the max-flow technique. In addition, we demonstrate that the size of the system being computed and the number of computations is bounded and is practical for real time control