Agent-Based Models and Platforms for Parallel Evolutionary Algorithms

Kisiel-Dorohinicki, Marek

doi:10.1007/978-3-540-24688-6_84

Cited by 16 publications

(9 citation statements)

References 7 publications

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“…It is related to and extends our previous publications regarding the implementation of effective tools for running population-based computational intelligence systems [4], especially using the agent paradigm [5], [6] in both parallel and distributed [7], as well as heterogeneous environments [8].…”

Section: Introductionsupporting

confidence: 53%

Efficient parallel execution of genetic algorithms on Epiphany manycore processor

Faber

Boryczko

2016

Annals of Computer Science and Information Systems

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Abstract-Recent years have seen a growing trend towards the introduction of more advanced manycore processors. On the other hand, there is also a growing popularity for cheap, creditcard-sized, devices offering more and more advanced features and computational power.In this paper we evaluate Parallella -a small board with the Epiphany manycore coprocessor consisting of sixteen MIMD cores connected by a mesh network-on-a-chip. Our tests are based on classical genetic algorithms. We discuss some possible optimizations and issues that arise from the architecture of the board. Although we achieve significant speed improvements, there are issues, such us the limited local memory size and slow memory access, that make the implementation of efficient code for Parallella difficult.

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Section: Introductionsupporting

confidence: 53%

Efficient parallel execution of genetic algorithms on Epiphany manycore processor

Faber

Boryczko

2016

Annals of Computer Science and Information Systems

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“…8). Because of the space limitation in this section there are presented results obtained during solving problems CTP1-4 however general tendencies of results obtained during our experiments with the use of the rest of CTP problems (CTP5-8) are similar to those presented in this section.Presented in this paper results were obtained with the use of the following values of the most important energetic parameters: ω 15 (fundamental quantum of life energy transferred among agents): 100, ω 12 (reproduction threshold): Algorithm 3 meetFN(a 1 ∈ s 1 , a 2 ∈ s 2 ) in conEMAS1: trans f er(a 1 , a 2, i ω 15 a 2 r γ 1 ) 2: i ω 11 a 2 = min(i ω 11 a 2 , a 1 a 2 ) er(a 1 , a 2 , i ω 15 a 2 r γ 1 ) 5: end if 6: if a 2 a 1 then7: trans f er(a 2 , a 1 , i ω 15 a 1 r γ 1 ) 8: end if 9: if a 2 a 1 then10: checkLevelO f Domination(a 1 , a 2 ) 11:…”

supporting

confidence: 52%

“…In the case of (con)EMAS approach such distribution seems to be simple both from the conceptual as well as implementation point of view-since each island with all their agents can be run on separated computational unit [8] (present realization of (con)EMAS platform was designed for such computations-but presented here results are coming from computations run on one single computational unit. It is worth to mention that distribution of computations in the case of NSGA-II or SPEA2 algorithms can be more difficult since for instance nondominated sorting, or crowding mechanisms in NSGA-II assumes the global knowledge about the whole population, so communication mark-up can be really significant in the case of mentioned classical algorithms, or even potentially changes in definition of those algorithms can be required to adapt them to distributed and parallel computations [2].…”

Section: Discussionmentioning

confidence: 98%

Efficient Constrained Evolutionary Multi-Agent System for Multi-objective Optimization

Siwik

Sikorski

2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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Evolutionary Multi-agent System approach for optimization (especially for multi-objective optimization) is a very promising computational model. Its computational as well as implemental simplicity causes that approaches based on EMAS model can be widely used for solving optimization tasks. It turns out that introducing some additional mechanisms into basic EMAS-causes that EMAS-based system can be successfully applied for solving constrained multi-objective optimization tasks-and what is important results obtained by proposed approach are better/not worse than results obtained by NSGA-II or SPEA2 algorithms. In the course of this paper some extensions that can be introduced into EMAS in order to constrained multi-objective optimization are presented. What is important-any new additional mechanisms do not have to be introduced into EMAS to solve constrained optimization tasks-the only extensions causing that EMAS-based model becomes an efficient and simple both in conception as well as in implementation-is an appropriate strategy regarding the flow among agents crucial non-renewable resource which is usually called life energy. In this paper, both the idea as well as preliminary results of Constrained Evolutionary MultiAgent System (conEMAS) for Multi-objective Optimization are presented.

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“…1). Each island represents a kind of local environment, where agents can perform their tasks [5]. The environment is organized with the given topology, so islands are connected with directed paths.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Elitist Evolutionary Multi-Agent System in solving noisy multi-objective optimization problems

Siwik

Sroka

Psiuk

2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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Evolutionary Multi-Agent System approach for optimization (for multi-objective optimization in particular) is a promising computational model. Its computational as well as implemental simplicity cause that approaches based on EMAS model can be widely used for solving optimization tasks. It turns out that introducing some additional mechanisms into basic EMAS-such as presented in the course of this paper elitist extensions cause that results obtained with the use of proposed elEMAS (elitist Evolutionary Multi-Agent System) approach are as high-quality results as results obtained by such famous and commonly used algorithms as NSGA-II or SPEA2. Apart from the computational simplicity especially important and interesting aspects of EMAS-based algorithms it is characteristic for them a kind of soft selection which can be additionally easily adjusted depending on a particular situation-in particular it is possible to introduce auto-adapting selection into such systems. Such a kind of selection seems to be especially important and valuable in solving optimization tasks in uncertain or "noised" environments. In the course of this paper the model and experimental results obtained by elEMAS system in solving noisy multi-objective optimization problems are presented and the general conclusion is as follows: EMAS-based optimization system seems to be more effective alternative than classical (i.e. non agent-based) evolutionary algorithms for multi-objective optimization, in particular, in uncertain environment, it seems to be better alternative than NSGA-II algorithm.

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Agent-Based Models and Platforms for Parallel Evolutionary Algorithms

Cited by 16 publications

References 7 publications

Efficient parallel execution of genetic algorithms on Epiphany manycore processor

Efficient parallel execution of genetic algorithms on Epiphany manycore processor

Efficient Constrained Evolutionary Multi-Agent System for Multi-objective Optimization

Elitist Evolutionary Multi-Agent System in solving noisy multi-objective optimization problems

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