Peter Lichodzijewski

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– Hierarchical SOMs are applied to the problem of host based intrusion detection on computer networks. Unlike systems based on operating system audit trails, the approach operates on real-time data without extensive off-line training and with minimal expert knowledge. Specific recommendations are made regarding the representation of time, network parameters(More)
Bid-based Genetic Programming (GP) provides an elegant mechanism for facilitating cooperative problem decomposition without an <i>a priori</i> specification of the number of team members. This is in contrast to existing teaming approaches where individuals learn a direct input-output map (e.g., from exemplars to class labels), allowing the approach to scale(More)
The computational overhead of genetic programming (GP) may be directly addressed without recourse to hardware solutions using active learning algorithms based on the random or dynamic subset selection heuristics (RSS or DSS). This correspondence begins by presenting a family of hierarchical DSS algorithms: RSS-DSS, cascaded RSS-DSS, and the balanced block(More)
A bid-based approach for coevolving Genetic Programming classifiers is presented. The approach coevolves a population of learners thatdecompose the instance space by way of their aggregate bidding behaviour. To reduce computation overhead, a small, relevant, subsetof training exemplars is (competitively) coevolved alongside the learners. The approach solves(More)
Models of Genetic Programming (GP) frequently reflect a neo-Darwinian view to evolution in which inheritance is based on a process of gradual refinement and the resulting solutions take the form of single monolithic programs. Conversely, introducing an explicitly symbiotic model of inheritance makes a divide-and-conquer metaphor for problem decomposition(More)
In this work a cooperative, bid-based, model for problem decomposition is proposed with application to discrete action domains such as classification. This represents a significant departure from models where each individual constructs a direct input-outcome map, for example, from the set of exemplars to the set of class labels as is typical under the(More)
Classification under large attribute spaces represents a dual learning problem in which attribute subspaces need to be identified at the same time as the classifier design is established. Embedded as opposed to filter or wrapper methodologies address both tasks simultaneously. The motivation for this work stems from the observation that team based(More)
Adopting a symbiotic model of evolution separates context for deploying an action from the action itself. Such a separation provides a mechanism for task decomposition in temporal sequence learning. Moreover, previously learned policies are taken to be synonymous with meta actions (actions that are themselves policies). Should solutions to the task not be(More)
Abslrocl-The cascade architecture for incremental learning is demonstrated within the context of genetic programming. Such a scheme provides the basis for building steadily more complex models until a desired degree of accuracy is reached. The architecture is demonstrated for several data mining dntasets. Efficient training on standard computing platforms(More)