We present an approach for parallel distributed implementation of genetic programming, which is devoted to improve the computational performance of genetic programming by exploiting parallelism at the level of evaluation of the individuals. The approach is based on DCOM client-server model. Using the DCOM-paradigm offers the advantages of parallel distributed implementation of genetic programming, such as binary standardization, platform-, machine- and protocol-neutrality, and seamless integration with different Internet protocols. The developed implementation of genetic programming runs in LAN and/or Internet environments.
The double-queued multi-threaded architecture of the DCOM-server, aimed to extend the functionality of the DCOM with features, such as asynchronous communications still implementing blocking-mode calls, and reduced communication overhead doing empty calls, is developed. The implementation of batching, directed towards the alleviation of communication overhead doing empty calls, is proposed. Analytically estimated and experimentally obtained performance evaluation results are discussed. The results show that clear super linear speedup can be achieved upon code growth in genetic programming.