SUIF: an infrastructure for research on parallelizing and optimizing compilers
ACM SIGPLAN Notices
An introduction to genetic algorithms
An introduction to genetic algorithms
Compact, multilayer layout for butterfly fat-tree
Proceedings of the twelfth annual ACM symposium on Parallel algorithms and architectures
Programming Language Concepts
Towards nanocomputer architecture
CRPIT '02 Proceedings of the seventh Asia-Pacific conference on Computer systems architecture
Exploiting multiple functionality for nano-scale reconfigurable systems
Proceedings of the 13th ACM Great Lakes symposium on VLSI
Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems
Automatic compilation to a coarse-grained reconfigurable system-opn-chip
ACM Transactions on Embedded Computing Systems (TECS)
On-chip traffic modeling and synthesis for MPEG-2 video applications
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
ASPLOS XI Proceedings of the 11th international conference on Architectural support for programming languages and operating systems
Ontogenetic development and fault tolerance in the POEtic tissue
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
QCADesigner: a rapid design and Simulation tool for quantum-dot cellular automata
IEEE Transactions on Nanotechnology
Fault-tolerance in nanocomputers: a cellular array approach
IEEE Transactions on Nanotechnology
A membrane computing system mapped on an asynchronous, distributed computational environment
WMC'05 Proceedings of the 6th international conference on Membrane Computing
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Evolutionary computation has been often used by computer scientists to evolve the morphologies and control systems of artificial life. Artificial 'brains', behaviour strategies, methods of communication, distributed problem solving and many other topics are commonly explored by using genetic algorithms and other evolutionary search techniques. We think that this approach may provide the general guidelines to efficiently manage and "design" computation on large and homogeneous lattices of simple, asynchronously interacting processing elements. Because of their structural simplicity, this kind of substrates will be suitable architectural models for computational machines based on molecular scale devices. In this paper we present an environment named Bio-molecular Engine (BME), in which different substrates can be simulated and used as "artificial worlds" where computational entities can rise, grow and evolve. In particular we discuss how to use a grid to evolutionary find a good solution to a well defined design issue: how much parallelism is good for a given problem computed in our environment.