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RGFGA: An efficient representation and crossover for grouping genetic algorithms
There is substantial research into genetic algorithms that are used to group large numbers of
objects into mutually exclusive subsets based upon some fitness function. However, nearly all
methods involve degeneracy to some degree.
We introduce a new representation for grouping genetic algorithms, the restricted growth function
genetic algorithm, that effectively removes all degeneracy, resulting in a more efficient search. A new crossover operator is also described that exploits a measure of similarity between chromosomes in a population. Using several synthetic datasets, we compare the performance of our representation and crossover with another well known state-of-the-art GA method, a strawman
optimisation method and a well-established statistical clustering algorithm, with encouraging results
Fitness landscape of the cellular automata majority problem: View from the Olympus
In this paper we study cellular automata (CAs) that perform the computational
Majority task. This task is a good example of what the phenomenon of emergence
in complex systems is. We take an interest in the reasons that make this
particular fitness landscape a difficult one. The first goal is to study the
landscape as such, and thus it is ideally independent from the actual
heuristics used to search the space. However, a second goal is to understand
the features a good search technique for this particular problem space should
possess. We statistically quantify in various ways the degree of difficulty of
searching this landscape. Due to neutrality, investigations based on sampling
techniques on the whole landscape are difficult to conduct. So, we go exploring
the landscape from the top. Although it has been proved that no CA can perform
the task perfectly, several efficient CAs for this task have been found.
Exploiting similarities between these CAs and symmetries in the landscape, we
define the Olympus landscape which is regarded as the ''heavenly home'' of the
best local optima known (blok). Then we measure several properties of this
subspace. Although it is easier to find relevant CAs in this subspace than in
the overall landscape, there are structural reasons that prevent a searcher
from finding overfitted CAs in the Olympus. Finally, we study dynamics and
performance of genetic algorithms on the Olympus in order to confirm our
analysis and to find efficient CAs for the Majority problem with low
computational cost
Computing Aggregate Properties of Preimages for 2D Cellular Automata
Computing properties of the set of precursors of a given configuration is a
common problem underlying many important questions about cellular automata.
Unfortunately, such computations quickly become intractable in dimension
greater than one. This paper presents an algorithm --- incremental aggregation
--- that can compute aggregate properties of the set of precursors
exponentially faster than na{\"i}ve approaches. The incremental aggregation
algorithm is demonstrated on two problems from the two-dimensional binary Game
of Life cellular automaton: precursor count distributions and higher-order mean
field theory coefficients. In both cases, incremental aggregation allows us to
obtain new results that were previously beyond reach
Universality classes of interaction structures for NK fitness landscapes
Kauffman's NK-model is a paradigmatic example of a class of stochastic models
of genotypic fitness landscapes that aim to capture generic features of
epistatic interactions in multilocus systems. Genotypes are represented as
sequences of binary loci. The fitness assigned to a genotype is a sum of
contributions, each of which is a random function defined on a subset of loci. These subsets or neighborhoods determine the genetic interactions of
the model. Whereas earlier work on the NK model suggested that most of its
properties are robust with regard to the choice of neighborhoods, recent work
has revealed an important and sometimes counter-intuitive influence of the
interaction structure on the properties of NK fitness landscapes. Here we
review these developments and present new results concerning the number of
local fitness maxima and the statistics of selectively accessible (that is,
fitness-monotonic) mutational pathways. In particular, we develop a unified
framework for computing the exponential growth rate of the expected number of
local fitness maxima as a function of , and identify two different
universality classes of interaction structures that display different
asymptotics of this quantity for large . Moreover, we show that the
probability that the fitness landscape can be traversed along an accessible
path decreases exponentially in for a large class of interaction structures
that we characterize as locally bounded. Finally, we discuss the impact of the
NK interaction structures on the dynamics of evolution using adaptive walk
models.Comment: 61 pages, 9 figure
Non-invasive Techniques Towards Recovering Highly Secure Unclonable Cryptographic Keys and Detecting Counterfeit Memory Chips
Due to the ubiquitous presence of memory components in all electronic computing systems, memory-based signatures are considered low-cost alternatives to generate unique device identifiers (IDs) and cryptographic keys. On the one hand, this unique device ID can potentially be used to identify major types of device counterfeitings such as remarked, overproduced, and cloned. On the other hand, memory-based cryptographic keys are commercially used in many cryptographic applications such as securing software IP, encrypting key vault, anchoring device root of trust, and device authentication for could services. As memory components generate this signature in runtime rather than storing them in memory, an attacker cannot clone/copy the signature and reuse them in malicious activity. However, to ensure the desired level of security, signatures generated from two different memory chips should be completely random and uncorrelated from each other. Traditionally, memory-based signatures are considered unique and uncorrelated due to the random variation in the manufacturing process. Unfortunately, in previous studies, many deterministic components of the manufacturing process, such as memory architecture, layout, systematic process variation, device package, are ignored. This dissertation shows that these deterministic factors can significantly correlate two memory signatures if those two memory chips share the same manufacturing resources (i.e., manufacturing facility, specification set, design file, etc.). We demonstrate that this signature correlation can be used to detect major counterfeit types in a non-invasive and low-cost manner. Furthermore, we use this signature correlation as side-channel information to attack memory-based cryptographic keys. We validate our contribution by collecting data from several commercially available off-the-shelf (COTS) memory chips/modules and considering different usage-case scenarios
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