2,301 research outputs found
Evolutionary Strategies for the Design of Binary Linear Codes
The design of binary error-correcting codes is a challenging optimization
problem with several applications in telecommunications and storage, which has
also been addressed with metaheuristic techniques and evolutionary algorithms.
Still, all these efforts focused on optimizing the minimum distance of
unrestricted binary codes, i.e., with no constraints on their linearity, which
is a desirable property for efficient implementations. In this paper, we
present an Evolutionary Strategy (ES) algorithm that explores only the subset
of linear codes of a fixed length and dimension. To that end, we represent the
candidate solutions as binary matrices and devise variation operators that
preserve their ranks. Our experiments show that up to length , our ES
always converges to an optimal solution with a full success rate, and the
evolved codes are all inequivalent to the Best-Known Linear Code (BKLC) given
by MAGMA. On the other hand, for larger lengths, both the success rate of the
ES as well as the diversity of the evolved codes start to drop, with the
extreme case of codes which all turn out to be equivalent to MAGMA's
BKLC.Comment: 15 pages, 3 figures, 3 table
On coding labeled trees
Trees are probably the most studied class of graphs in Computer Science. In this thesis we study bijective codes that represent labeled trees by means of string of node labels. We contribute to the understanding of their algorithmic tractability, their properties, and their applications.
The thesis is divided into two parts. In the first part we focus on two types of tree codes, namely Prufer-like codes and Transformation codes. We study optimal encoding and decoding algorithms, both in a sequential and in a parallel setting. We propose a unified approach that works for all Prufer-like codes and a more generic scheme based on the transformation of a tree into a functional digraph suitable for all bijective codes. Our results in this area close a variety of open problems.
We also consider possible applications of tree encodings, discussing how to exploit these codes in Genetic Algorithms and in the generation of random trees. Moreover, we introduce a modified version of a known code that, in Genetic Algorithms, outperform all the other known codes.
In the second part of the thesis we focus on two possible generalizations of our work. We first take into account the classes of k-trees and k-arch graphs (both superclasses of trees): we study bijective codes for this classes of graphs and their algorithmic feasibility. Then, we shift our attention to Informative Labeling Schemes. In this context labels are no longer considered as simple unique node identifiers, they rather convey information useful to achieve efficient computations on the tree. We exploit this idea to design a concurrent data structure for the lowest common ancestor problem on dynamic trees.
We also present an experimental comparison between our labeling scheme and the one proposed by Peleg for static trees
On the Evolution of Boomerang Uniformity in Cryptographic S-boxes
S-boxes are an important primitive that help cryptographic algorithms to be
resilient against various attacks. The resilience against specific attacks can
be connected with a certain property of an S-box, and the better the property
value, the more secure the algorithm. One example of such a property is called
boomerang uniformity, which helps to be resilient against boomerang attacks.
How to construct S-boxes with good boomerang uniformity is not always clear.
There are algebraic techniques that can result in good boomerang uniformity,
but the results are still rare. In this work, we explore the evolution of
S-boxes with good values of boomerang uniformity. We consider three different
encodings and five S-box sizes. For sizes and , we
manage to obtain optimal solutions. For , we obtain optimal
boomerang uniformity for the non-APN function. For larger sizes, the results
indicate the problem to be very difficult (even more difficult than evolving
differential uniformity, which can be considered a well-researched problem).Comment: 15 pages, 3 figures, 4 table
GA: A Package for Genetic Algorithms in R
Genetic algorithms (GAs) are stochastic search algorithms inspired by the basic principles of biological evolution and natural selection. GAs simulate the evolution of living organisms, where the fittest individuals dominate over the weaker ones, by mimicking the biological mechanisms of evolution, such as selection, crossover and mutation. GAs have been successfully applied to solve optimization problems, both for continuous (whether differentiable or not) and discrete functions.
This paper describes the R package GA, a collection of general purpose functions that provide a flexible set of tools for applying a wide range of genetic algorithm methods. Several examples are discussed, ranging from mathematical functions in one and two dimensions known to be hard to optimize with standard derivative-based methods, to some selected statistical problems which require the optimization of user defined objective functions. (This paper contains animations that can be viewed using the Adobe Acrobat PDF viewer.
A Tutorial on Clique Problems in Communications and Signal Processing
Since its first use by Euler on the problem of the seven bridges of
K\"onigsberg, graph theory has shown excellent abilities in solving and
unveiling the properties of multiple discrete optimization problems. The study
of the structure of some integer programs reveals equivalence with graph theory
problems making a large body of the literature readily available for solving
and characterizing the complexity of these problems. This tutorial presents a
framework for utilizing a particular graph theory problem, known as the clique
problem, for solving communications and signal processing problems. In
particular, the paper aims to illustrate the structural properties of integer
programs that can be formulated as clique problems through multiple examples in
communications and signal processing. To that end, the first part of the
tutorial provides various optimal and heuristic solutions for the maximum
clique, maximum weight clique, and -clique problems. The tutorial, further,
illustrates the use of the clique formulation through numerous contemporary
examples in communications and signal processing, mainly in maximum access for
non-orthogonal multiple access networks, throughput maximization using index
and instantly decodable network coding, collision-free radio frequency
identification networks, and resource allocation in cloud-radio access
networks. Finally, the tutorial sheds light on the recent advances of such
applications, and provides technical insights on ways of dealing with mixed
discrete-continuous optimization problems
- …