- author = "Sven E. Eklund",
- title = "A Massively Parallel Architecture for Linear Machine Code Genetic Programming",
- booktitle = "Evolvable Systems: From Biology to Hardware: Proceedings of 4th International Conference, ICES 2001",
- year = "2001",
- editor = "Yong Liu and Kiyoshi Tanaka and Masaya Iwata and Tetsuya Higuchi and Moritoshi Yasunaga",
- volume = "2210",
- series = "Lecture Notes in Computer Science",
- pages = "216--224",
- address = "Tokyo, Japan",
- publisher_address = "Heidelberg",
- month = oct # " 3-5",
- publisher = "Springer-Verlag",
- keywords = "genetic algorithms, genetic programming",
- CODEN = "LNCSD9",
- ISSN = "0302-9743",
- bibdate = "Sat Feb 2 13:06:57 MST 2002",
-
DOI = "
doi:10.1007/3-540-45443-8_19",
- acknowledgement = ack-nhfb,
- abstract = "Over the last decades Genetic Algorithms (GA) and Genetic Programming (GP) have proven to be efficient tools for a wide range of applications. However, in order to solve human-competitive problems they require large amounts of computational power, particularly during fitness calculations. In this paper I propose the implementation of a massively parallel model in hardware in order to speed up GP. This fine-grained diffusion architecture has the advantage over the popular Island model of being VLSI-friendly and is therefore small and portable, without sacrificing scalability and effectiveness. The diffusion architecture consists of a large amount of independent processing nodes, connected through an X-net topology, that evolve a large number of small, overlapping sub-populations. Every node has its own embedded CPU, which executes a linear machine code representation of the individuals. Preliminary simulation results (low-level VHDL simulation) indicate a performance of 10.000 generations per second (depending on the application). One node requires 10-20.000 gates including the CPU (also application dependent), which makes it possible to fit up to 2.000 individuals in one FPGA (Virtex XC2V10000).",
-
notes = "ICES-2001
A1 Dalarna University, Sweden sven.eklund@ieee.org",
