As computing power grows, the automated specialization and design of evolutionary algorithms (EAs) to tune their performance to individual problem classes becomes more attractive. To this end, a significant amount of research has been conducted in recent decades, utilizing a wide range of techniques. However, few techniques have been devised which automate the design of the overall structure of an EA. Most EA implementations rely solely on the traditional evolutionary cycle of parent selection, reproduction, and survival selection, and those with unique structures typically replace this with another static, hand-made cycle. Existing techniques for modifying the evolutionary structure use representations which are either loosely structured and highly stochastic, or which are constrained and unable to easily evolve complicated pathways. The ability to easily evolve complex evolutionary pathways would greatly expand the heuristic space which can be explored during specialization, potentially allowing for the representation of EAs which outperform the traditional cycle. This work proposes a methodology for the automated design of the evolutionary process by introducing a novel directed-graph-based representation, created to be mutable and flexible, permitting a black-box designer to produce reusable, high-performance EAs. Experiments show that our methodology can produce high-performance EAs demonstrating intelligible strategies.

Network segmentation has a variety of applications, including computer network security. A well segmented computer network is less likely to result in information leaks and more resilient to adversarial traversal. Conventionally network segmentation approaches rely on graph partitioning algorithms. However, general-purpose graph partitioning solutions are just that, general purpose. These approaches do not exploit specific topological characteristics present in certain classes of networks. Tailored partition methods can be developed to target specific domains, but this process can be time consuming and difficult. This work builds on previous research employing generative hyper-heuristics in the form of genetic programming for automating the development of customized graph partitioning heuristics by incorporating a dynamic approach to controlling the granularity of the heuristic search. The potential of this approach is demonstrated using two real-world complex network applications. Results show that the automated design process is capable of fine tuning graph partitioning heuristics that sacrifice generality for improved performance on targeted networks.

As large scale enterprise computer networks become more ubiquitous, finding the appropriate balance between user convenience and user access control is an increasingly challenging proposition. Suboptimal partitioning of users' access and available services contributes to the vulnerability of enterprise networks. Previous edge-cut partitioning methods unduly restrict users' access to network resources. This paper introduces a novel method of network partitioning superior to the current state-of-the-art which minimizes user impact by providing alternate avenues for access that reduce vulnerability. Networks are modeled as bipartite authentication access graphs and a multi-objective evolutionary algorithm is used to simultaneously minimize the size of large connected components while minimizing overall restrictions on network users. Results are presented on a real world data set that demonstrates the effectiveness of the introduced method compared to previous naive methods.