Distributed Genetic Programming Framework
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Package org.dgpf.search.api.p2p

Here you can find all the information about the org.dgpf.search.api.p2p package. You may explore the package using the directory listing below. This listing contains all the files contained directly in this package and all its sub-packages. There you can either download a file directly (by clicking on the "download"-link) or read it online (by clicking on its direct link left in the directoy listing. Further information about the package's contents is given at the bottom of this page.

Directory Listing

  ├[adaptation]2018-01-07 12:03:43 GMT+0000
  ├CircularIndividualBuffer.javadownload download3.230 KB2018-01-07 12:03:35 GMT+0000
  ├IndividualEmigrator.javadownload download5.059 KB2018-01-07 12:03:35 GMT+0000
  ├P2PAdaptationStrategy.javadownload download3.772 KB2018-01-07 12:03:35 GMT+0000
  ├P2PData.javadownload download15.482 KB2018-01-07 12:03:35 GMT+0000
  ├P2PParameters.javadownload download6.866 KB2018-01-07 12:03:35 GMT+0000
  ├P2PSearchUtils.javadownload download3.351 KB2018-01-07 12:03:35 GMT+0000
  ├P2PState.javadownload download13.570 KB2018-01-07 12:03:35 GMT+0000
  ├P2PStateBag.javadownload download3.659 KB2018-01-07 12:03:35 GMT+0000
  ├package.htmldownload download1.122 KB2018-01-07 12:03:35 GMT+0000
  ├SearchP2P.javadownload download14.013 KB2018-01-07 12:03:35 GMT+0000
  └StatefulP2PAdaptationStrategy.javadownload download2.638 KB2018-01-07 12:03:35 GMT+0000

The Peer-To-Peer node used by the arbitrary search algorithms to exchange individuals is defined here.

Package Specification

Using the Distributed Genetic Programming Framework, one can easily distribute search algorithms lik a genetic evolutions over a network of computers or even the internet. This is done by letting each node run its own search algorithm, exchanging individuals using peer-to-peer system.

The structure of the peer-to-peer extension for searches allows you not only to create homogeneous searches (like cooperating Genetic Algorithms using the "Island Hopping" paradigm). Now you can incorporate heterogeneous searches, like having two nodes running Simulated Annealing and one running a Genetic Algorithm. These different searches can exchange solution candidates (so-called individuals) and thus cooperate. This allows you to make use of the different, good properties of all search algorithms and will speed up your search and improve solution quality

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