Jul 28, 1996 · We provide evidence for the effectiveness of our DGP by comparative experiments with the partitioned GP, i.e. a DGP without migration, and the canonical GP. In order to analyze the performance, we discuss the change of the structural diversity with generations for solving the deceptive problem.

Oct 13, 2022 · Cartesian genetic programming is a popular version of genetic programming and has meanwhile proven its performance in many use cases. This paper introduces an algorithmic level decomposition of program evolution that can be solved by a multi-agent system in a fully distributed manner.

Sep 1, 2015 · Provide an updated and systematic review of distributed evolutionary algorithms. Classify the models into population and dimension-distributed groups semantically. Highlight recent research hotspots in this field. Discuss challenges and potential research directions in …

AGPS is a portable, distributed genetic pro-gramming system, implemented on MPI. AGPS views processors as a bounded resource and op-timizes the use of that resource by dynamically varying the number of processors that it uses during execution, adapting to the external de-mand for those processors.

Jan 1, 1999 · This paper presents a novel Genetic Parallel Programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP). The MAP is a Multiple Instruction-streams, Multiple Data-streams (MIMD), general-...

This paper presents a Genetic Programming algorithm which applies a clustering algorithm. The method evolves a population of trees for a fixed number of rounds or generations and applies a clustering algorithm to the population, in a way that in the selection process...

Jul 28, 1996 · This paper introduces a distributed parallel implementation of GP (Genetic Programming), DGP. We implemented our DGP on the parallel system AP-1000+ of Fujitsu, which consisting of 32 processors. Several experimental results are shown comparing three architectures of DGP, i.e. the ring type, the one-way torus, and the two-way torus.

In our work, we test six different ways for representing distributed programs, compris-ing adaptations and extensions of well-known Genetic Programming methods (SGP, eSGP, and LGP), one bio-inspired approach (Fraglets), and two new program representations called Rule-based Genetic Programming (RBGP, eRBGP) designed by us. We breed programs in

In this paper, we introduce a new parallel and distributed genetic programming system, with the objective of mitigating this drawback.

This paper describes PDGP (Parallel Distributed Genetic Programming), a new form of genetic programming which is suitable for the development of fine-grained parallel programs.