11,189 research outputs found
Cryptanalysis of SDES via evolutionary computation techniques
The cryptanalysis of simplified data encryption standard can be formulated as
NP-Hard combinatorial problem. The goal of this paper is two fold. First we
want to make a study about how evolutionary computation techniques can
efficiently solve the NP-Hard combinatorial problem. For achieving this goal we
test several evolutionary computation techniques like memetic algorithm,
genetic algorithm and simulated annealing for the cryptanalysis of simplified
data encryption standard problem (SDES). And second was a comparison between
memetic algorithm, genetic algorithm and simulated annealing were made in order
to investigate the performance for the cryptanalysis on SDES. The methods were
tested and extensive computational results show that memetic algorithm performs
better than genetic algorithms and simulated annealing for such type of NP-Hard
combinatorial problem. This paper represents our first effort toward efficient
memetic algorithm for the cryptanalysis of SDES.Comment: 7 Pages, International Journal of Computer Science and Information
Security (IJCSIS
On the Impact of the Migration Topology on the Island Model
Parallel Global Optimization Algorithms (PGOA) provide an efficient way of
dealing with hard optimization problems. One method of parallelization of GOAs
that is frequently applied and commonly found in the contemporary literature is
the so-called Island Model (IM). In this paper we analyze the impact of the
migration topology on the performance of a PGOA which uses the Island Model. In
particular we consider parallel Differential Evolution and Simulated Annealing
with Adaptive Neighborhood and draw first conclusions that emerge from the
conducted experiments.Comment: Accepted in Parallel Computing
Evolutionary Synthesis of Fractional Capacitor Using Simulated Annealing Method
Synthesis of fractional capacitor using classical analog circuit synthesis method was described in [6]. The work presented in this paper is focused on synthesis of the same problem by means of evolutionary method simulated annealing. Based on given desired characteristic function as input impedance or transfer function, the proposed method is able to synthesize topology and values of the components of the desired analog circuit. Comparison of the results given in [6] and results obtained by the proposed method will be given and discussed
Tree Search vs Optimization Approaches for Map Generation
Search-based procedural content generation uses stochastic global
optimization algorithms to search for game content. However, standard tree
search algorithms can be competitive with evolution on some optimization
problems. We investigate the applicability of several tree search methods to
level generation and compare them systematically with several optimization
algorithms, including evolutionary algorithms. We compare them on three
different game level generation problems: Binary, Zelda, and Sokoban. We
introduce two new representations that can help tree search algorithms deal
with the large branching factor of the generation problem. We find that in
general, optimization algorithms clearly outperform tree search algorithms, but
given the right problem representation certain tree search algorithms perform
similarly to optimization algorithms, and in one particular problem, we see
surprisingly strong results from MCTS.Comment: 10 pages, 9 figures, published at AIIDE 202
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives
Particle Swarm Optimization (PSO) is a metaheuristic global optimization
paradigm that has gained prominence in the last two decades due to its ease of
application in unsupervised, complex multidimensional problems which cannot be
solved using traditional deterministic algorithms. The canonical particle swarm
optimizer is based on the flocking behavior and social co-operation of birds
and fish schools and draws heavily from the evolutionary behavior of these
organisms. This paper serves to provide a thorough survey of the PSO algorithm
with special emphasis on the development, deployment and improvements of its
most basic as well as some of the state-of-the-art implementations. Concepts
and directions on choosing the inertia weight, constriction factor, cognition
and social weights and perspectives on convergence, parallelization, elitism,
niching and discrete optimization as well as neighborhood topologies are
outlined. Hybridization attempts with other evolutionary and swarm paradigms in
selected applications are covered and an up-to-date review is put forward for
the interested reader.Comment: 34 pages, 7 table
Simulated annealing: in mathematical global optimization computation, hybrid with local or global search, and practical applications in crystallography and molecular modelling
Simulated annealing (SA) was inspired from annealing in metallurgy, a
technique involving heating and controlled cooling of a material to increase
the size of its crystals and reduce their defects, both are attributes of the
material that depend on its thermodynamic free energy. In this Paper, firstly
we will study SA in details on its practical implementation. Then, hybrid pure
SA with local (or global) search optimization methods allows us to be able to
design several effective and efficient global search optimization methods. In
order to keep the original sense of SA, we clarify our understandings of SA in
crystallography and molecular modeling field through the studies of prion
amyloid fibrils
Extremal Optimization: an Evolutionary Local-Search Algorithm
A recently introduced general-purpose heuristic for finding high-quality
solutions for many hard optimization problems is reviewed. The method is
inspired by recent progress in understanding far-from-equilibrium phenomena in
terms of {\em self-organized criticality,} a concept introduced to describe
emergent complexity in physical systems. This method, called {\em extremal
optimization,} successively replaces the value of extremely undesirable
variables in a sub-optimal solution with new, random ones. Large,
avalanche-like fluctuations in the cost function self-organize from this
dynamics, effectively scaling barriers to explore local optima in distant
neighborhoods of the configuration space while eliminating the need to tune
parameters. Drawing upon models used to simulate the dynamics of granular
media, evolution, or geology, extremal optimization complements approximation
methods inspired by equilibrium statistical physics, such as {\em simulated
annealing}. It may be but one example of applying new insights into {\em
non-equilibrium phenomena} systematically to hard optimization problems. This
method is widely applicable and so far has proved competitive with -- and even
superior to -- more elaborate general-purpose heuristics on testbeds of
constrained optimization problems with up to variables, such as
bipartitioning, coloring, and satisfiability. Analysis of a suitable model
predicts the only free parameter of the method in accordance with all
experimental results.Comment: Latex, 17 pages, to appear in the {\it Proceedings of the 8th INFORMS
Computing Society Conference,} (2003
Review of Metaheuristics and Generalized Evolutionary Walk Algorithm
Metaheuristic algorithms are often nature-inspired, and they are becoming
very powerful in solving global optimization problems. More than a dozen of
major metaheuristic algorithms have been developed over the last three decades,
and there exist even more variants and hybrid of metaheuristics. This paper
intends to provide an overview of nature-inspired metaheuristic algorithms,
from a brief history to their applications. We try to analyze the main
components of these algorithms and how and why they works. Then, we intend to
provide a unified view of metaheuristics by proposing a generalized
evolutionary walk algorithm (GEWA). Finally, we discuss some of the important
open questions.Comment: 14 page
A Heuristic for Magic and Antimagic Graph Labellings
Graph labellings have been a very fruitful area of research in the last four
decades. However, despite the staggering number of papers published in the
field (over 1000), few general results are available, and most papers deal with
particular classes of graphs and methods. Here we approach the problem from the
computational viewpoint, and in a quite general way. We present the existence
problem of a particular labelling as a combinatorial optimization problem, then
we discuss the possible strategies to solve it, and finally we present a
heuristic for finding different classes of labellings, like vertex-, edge-, or
face-magic, and -antimagic -labellings. The algorithm has
been implemented in C++ and MATLAB, and with its aid we have been able to
derive new results for some classes of graphs, in particular, vertex-antimagic
edge labellings for small graphs of the type , for which no
general construction is known so far
A Computational Study of Genetic Crossover Operators for Multi-Objective Vehicle Routing Problem with Soft Time Windows
The article describes an investigation of the effectiveness of genetic
algorithms for multi-objective combinatorial optimization (MOCO) by presenting
an application for the vehicle routing problem with soft time windows. The work
is motivated by the question, if and how the problem structure influences the
effectiveness of different configurations of the genetic algorithm.
Computational results are presented for different classes of vehicle routing
problems, varying in their coverage with time windows, time window size,
distribution and number of customers. The results are compared with a simple,
but effective local search approach for multi-objective combinatorial
optimization problems
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