A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
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We discuss the feasibility of applying the distributed collaborative approach for improving the computational performance of genetic programming (GP), implemented on cost-efficient clusters or the Internet. Proposed approach exploits the coarse grained inherent parallelism in GP among relatively autonomous subpopulations. Developed architecture of distributed collaborative parallel GP (DCPGP) features single, global migration broker and centralized manager of the semi-isolated subpopulations, which contribute to quick propagation of the globally fittest individuals among the subpopulations, reducing the performance demands to the underlying communication network, and achieving dynamic scaling-up features. DCPGP exploits the distributed component object model (DCOM) as a communication paradigm, which as a true system model offers generic support for the issues of naming, locating and security of communicating entities of developed architecture. Experimentally obtained speedup results show that close to linear speedup characteristics of the prototype of DCPGP are achieved on network of 8 workstations.