Parallelization is becoming a more important issue for solving difficult optimization problems. Island models combine phases of independent evolution with migration where genetic information is spread out to neighbore...
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Parallelization is becoming a more important issue for solving difficult optimization problems. Island models combine phases of independent evolution with migration where genetic information is spread out to neighbored islands. This can lead to an increased diversity within the population. Despite many successful applications, the reasons behind the success of island models are not well understood. We perform a first rigorous runtime analysis for island models and construct a function where phases of independent evolution as well as communication among the islands are essential. A simple island model with migration finds a global optimum in polynomial time, while panmictic populations as well as island models without migration need exponential time, with very high probability. Our results lead to new insights into the usefulness of migration, how information is propagated in island models, and how to set parameters such as the migration interval. This is a novel contribution to the theoretical foundation of parallel EAs. Further, we provide empirical results that complement the theoretical results, investigate the robustness with respect to the choice of the migration interval and compare various migration topologies using statistical tests.
DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to make algorithms explicit and data structures tr...
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DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to make algorithms explicit and data structures transparent, as opposed to the more common black-box frameworks. Freely available with extensive documentation at http://***, DEAP is an open source project under an LGPL license.
Estimation of distribution algorithms (EDAs) are one of the most promising paradigms in today's evolutionary computation. In this field, there has been an incipient activity in the so-called parallel estimation of...
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Estimation of distribution algorithms (EDAs) are one of the most promising paradigms in today's evolutionary computation. In this field, there has been an incipient activity in the so-called parallel estimation of distribution algorithms (pEDAs). One of these approaches is the distributed estimation of distribution algorithms (dEDAs). This paper introduces a new initialization mechanism for each of the populations of the islands based on the Voronoi cells. To analyze the results, a series of different experiments using the benchmark suite for the special session on Real-parameter Optimization of the IEEE CEC 2005 conference has been carried out. The results obtained suggest that the Voronoi initialization method considerably improves the performance obtained from a traditional uniform initialization.
DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to make algorithms explicit and data structures tr...
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DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to make algorithms explicit and data structures transparent, as opposed to the more common black-box frameworks. Freely available with extensive documentation at http://***, DEAP is an open source project under an LGPL license.
A distributed data mining algorithm to improve the detection accuracy when classifying malicious or unauthorized network activity is presented. The algorithm is based on genetic programming (GP) extended with the ense...
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A distributed data mining algorithm to improve the detection accuracy when classifying malicious or unauthorized network activity is presented. The algorithm is based on genetic programming (GP) extended with the ensemble paradigm. GP ensemble is particularly suitable for distributed intrusion detection because it allows to build a network profile by combining different classifiers that together provide complementary information. The main novelty of the algorithm is that data is distributed across multiple autonomous sites and the learner component acquires useful knowledge from this data in a cooperative way. The network profile is then used to predict abnormal behavior. Experiments on the KDD Cup 1999 Data show the capability of genetic programming in successfully dealing with the problem of intrusion detection on distributed data.
This article proposes a distributed differential evolution which employs a novel self-adaptive scheme, namely scale factor inheritance. In the proposed algorithm, the population is distributed over several sub-populat...
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This article proposes a distributed differential evolution which employs a novel self-adaptive scheme, namely scale factor inheritance. In the proposed algorithm, the population is distributed over several sub-populations allocated according to a ring topology. Each sub-population is characterized by its own scale factor value. With a probabilistic criterion, that individual displaying the best performance is migrated to the neighbor population and replaces a pseudo-randomly selected individual of the target sub-population. The target sub-population inherits not only this individual but also the scale factor if it seems promising at the current stage of evolution. In addition, a perturbation mechanism enhances the exploration feature of the algorithm. The proposed algorithm has been run on a set of various test problems and then compared to two sequential differential evolution algorithms and three distributed differential evolution algorithms recently proposed in literature and representing state-of-the-art in the field. Numerical results show that the proposed approach seems very efficient for most of the analyzed problems, and outperforms all other algorithms considered in this study.
In this work we propose a fine grained approach with self-adaptive migration rate for distributedevolutionary computation. Our target is to gain some insights on the effects caused by communication when the algorithm...
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In this work we propose a fine grained approach with self-adaptive migration rate for distributedevolutionary computation. Our target is to gain some insights on the effects caused by communication when the algorithm scales. To this end, we consider a set of basic topologies in order to avoid the overlapping of algorithmic effects between communication and topological structures. We analyse the approach viability by comparing how solution quality and algorithm speed change when the number of processors increases and compare it with an Island model based implementation. A finer-grained approach implies a better chance of achieving a larger scalable system;such a feature is crucial concerning large-scale parallel architectures such as peer-to-peer systems. In order to check scalability, we perform a threefold experimental evaluation of this model: first, we concentrate on the algorithmic results when the problem scales up to eight nodes in comparison with how it does following the Island model. Second, we analyse the computing time speedup of the approach while scaling. Finally, we analyse the network performance with the proposed self-adaptive migration rate policy that depends on the link latency and bandwidth. With this experimental setup, our approach shows better scalability than the Island model and a equivalent robustness on the average of the three test functions under study.
In aerodynamic shape optimization, the availability Of multiple evaluation models of different precision and hence computational cost can be efficiently exploited in a hierarchical evolutionary algorithm. Thus, in thi...
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In aerodynamic shape optimization, the availability Of multiple evaluation models of different precision and hence computational cost can be efficiently exploited in a hierarchical evolutionary algorithm. Thus, in this work the demes of a distributedevolutionary algorithm are ordered in levels, with each level employing a different flow analysis method, giving rise to a hierarchical distributed scheme. The arduous task of exploring the design space is undertaken by deities consisting the lower hierarchy level, which use a low-cost flow analysis tool, namely a viscous-inviscid flow interaction method. promising solutions are directed towards the higher level, where these are further evolved based on a high precision/cost evaluation tool, viz. a Navier-Stokes equations solver. The final, optimal solution is obtained front the highest hierarchy level. At each level, metamodels, trained on-line on the outcome of evaluations with the level's analysis tool, are used. The role of metamodels is to allow a parsimonious use of computational resources by filtering the poorly performing individuals in each deme. The entire algorithm has been implemented so as to take advantage of a parallel computing system. The efficiency and effectiveness of the proposed hierarchical distributedevolutionary algorithm have been assessed in the design of a transonic isolated airfoil and a compressor cascade. Remarkable superiority over the conventional evolutionaryalgorithms has been monitored. Copyright (c) 2006 John Wiley & Sons, Ltd.
The dynamical properties of structures, such as natural frequencies, damping ratios and mode shapes, can be obtained by several identification methods. Some are based on the direct signal processing in a time domain;o...
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The dynamical properties of structures, such as natural frequencies, damping ratios and mode shapes, can be obtained by several identification methods. Some are based on the direct signal processing in a time domain;others transform response data to the frequency domain. The development of these techniques is useful in the production of more accurate structural models;they can be also used to test the level of damage in structures (or verify their strength to support new load actions) by using experimental data. There are situations where frequency domain algorithms and conventional system identification techniques fail, do not allow adequate solution of the identification problems or become trapped in a local optimum. It is in these cases where evolutionary optimization techniques are important tools for evaluating the dynamical properties of structural systems in practical applications. This article presents a methodology to determine the dynamic properties of structures knowing their response in terms of displacement, velocities or accelerations in the time domain when they are subjected to a free vibration excitation. In order to do that, a specialized evolutionary algorithm capable of adapting its parameters to the different types of registers obtained from the dynamic time response of a structure is implemented in a robust way, making this approach useful in practical situations. A distributed real genetic algorithm (DRGA) based on an island model of different subpopulations is used to adjust a simulated response signal of a building to the real response signal. Initially, computer-generated synthetic response signals are used but, in future, the approach will be validated with signals obtained from free vibration experimental tests and will be extended to other types of dynamical excitation signals. Finally, the method will be tested with data obtained from earthquake events.
The optimization of such complex systems as manufacturing systems often necessitates the use of simulation. Unfortunately, most current existing methods for simulation optimization present certain major inconveniences...
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The optimization of such complex systems as manufacturing systems often necessitates the use of simulation. Unfortunately, most current existing methods for simulation optimization present certain major inconveniences. In particular, they generally cannot take into account nonnumerical variables (e.g., dispatching rules), they may be sensitive to local extremums and they are often time consuming. In this paper, the use of evolutionaryalgorithms is suggested for the optimization of simulation models. Several types of variables are taken into account. The reduction of computing cost is achieved through the parallelization of this method, which allows several simulation experiments to be run simultaneously. Emphasis is put on a distributed approach where several computers manage both their own local population of solutions and their own simulation experiments, exchanging solutions using a migration operator. After a first evaluation through a mathematical function with a known optimum, the benefits of this new approach are demonstrated through the example of a transport lot sizing and transporter allocation problem in a manufacturing flow shop system, which is solved using a distributed software implemented on a network of eight Sun workstations.
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