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Parameterless hierarchical BOA
 Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2004), Part II, LNCS 3103
, 2004
"... An automated technique has recently been proposed to transfer learning in the hierarchical Bayesian optimization algorithm (hBOA) based on distancebased statistics. The technique enables practitioners to improve hBOA efficiency by collecting statistics from probabilistic models obtained in previous ..."
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An automated technique has recently been proposed to transfer learning in the hierarchical Bayesian optimization algorithm (hBOA) based on distancebased statistics. The technique enables practitioners to improve hBOA efficiency by collecting statistics from probabilistic models obtained in previous hBOA runs and using the obtained statistics to bias future hBOA runs on similar problems. The purpose of this paper is threefold: (1) test the technique on several classes of NPcomplete problems, including MAXSAT, spin glasses and minimum vertex cover; (2) demonstrate that the technique is effective even when previous runs were done on problems of different size; (3) provide empirical evidence that combining transfer learning with other efficiency enhancement techniques can often provide nearly multiplicative speedups.
Attack Graphs for Sensor Placement, Alert Prioritization, and Attack Response
, 2007
"... We describe the optimal placement of intrusion detection system (IDS) sensors and prioritization of IDS alarms, using attack graph analysis. Our attack graphs predict the various possible ways of penetrating a network to reach critical assets. In particular, automated analysis of network configurati ..."
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We describe the optimal placement of intrusion detection system (IDS) sensors and prioritization of IDS alarms, using attack graph analysis. Our attack graphs predict the various possible ways of penetrating a network to reach critical assets. In particular, automated analysis of network configuration and attacker exploits provides an attack graph showing all possible paths to critical assets. We then place IDS sensors to cover all these paths, using the fewest number of sensors. The sensorplacement problem we pose is an instance of the NPhard minimal set cover problem, which we solve through a greedy algorithm. Through our approach, all traffic along vulnerable paths to critical resources is monitored, with no deployment of unnecessary sensors. Then, through our predictive vulnerabilitybased attack graphs, we prioritize IDS alarms based on their level of threat (attack graph distance) to critical assets. The predictive power of our attack graphs then provides the necessary context for appropriate attack response.
Transfer learning, soft distancebased bias, and the hierarchical boa
, 2012
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Learn from the Past: Improving ModelDirected . . . Distancebased Bias
, 2012
"... For many optimization problems it is possible to define a problemspecific distance metric over decision variables that correlates with the strength of interactions between the variables. Examples of such problems include additively decomposable functions, facility location problems, and atomic clus ..."
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For many optimization problems it is possible to define a problemspecific distance metric over decision variables that correlates with the strength of interactions between the variables. Examples of such problems include additively decomposable functions, facility location problems, and atomic cluster optimization. However, the use of such a metric for enhancing efficiency of optimization techniques is often not straightforward. This paper describes a framework that allows optimization practitioners to improve efficiency of modeldirected optimization techniques by combining such a distance metric with information mined from previous optimization runs on similar problems. The framework is demonstrated and empirically evaluated in the context of the hierarchical Bayesian optimization algorithm (hBOA). Experimental results provide strong empirical evidence that the proposed approach provides significant speedups and that it can be effectively combined with other efficiency enhancements. The paper demonstrates how straightforward it is to adapt the proposed framework to other modeldirected optimization techniques by presenting several examples.
Enhancing Efficiency of Hierarchical BOA via . . .
, 2008
"... This paper analyzes the effects of restricting probabilistic models in the hierarchical Bayesian optimization algorithm (hBOA) by defining a distance metric over variables and disallowing dependencies between variables at distances greater than a given threshold. We argue that by using prior problem ..."
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This paper analyzes the effects of restricting probabilistic models in the hierarchical Bayesian optimization algorithm (hBOA) by defining a distance metric over variables and disallowing dependencies between variables at distances greater than a given threshold. We argue that by using prior problemspecific knowledge, it is often possible to develop a distance metric that closely corresponds to the strength of interactions between variables. This distance metric can then be used to speed up model building in hBOA. Three test problems are considered: 3D Ising spin glasses, random additively decomposable problems, and the minimum vertex cover.