Random sequence generation by cellular automata
Advances in Applied Mathematics
Computer architecture: a quantitative approach
Computer architecture: a quantitative approach
Introduction to algorithms
Uniform crossover in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Using genetic algorithms to solve NP-complete problems
Proceedings of the third international conference on Genetic algorithms
Parallel genetic algorithms, population genetics and combinatorial optimization
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
SPAA '92 Proceedings of the fourth annual ACM symposium on Parallel algorithms and architectures
A compiling genetic programming system that directly manipulates the machine code
Advances in genetic programming
An introduction to genetic algorithms
An introduction to genetic algorithms
DAC '96 Proceedings of the 33rd annual Design Automation Conference
Principles of digital design
Parallel genetic programming: a scalable implementation using the transputer network architecture
Advances in genetic programming
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
A Hardware Genetic Algorithm for the Travelling Salesman Problem on SPLASH 2
FPL '95 Proceedings of the 5th International Workshop on Field-Programmable Logic and Applications
Implementing a genetic algorithm on a parallel custom computing machine
FCCM '95 Proceedings of the IEEE Symposium on FPGA's for Custom Computing Machines
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In this chapter, we present a survival-based, steady-state GA designed for efficient implementation in hardware and the design of a pipelined genetic algorithm processor that can generate one new, evaluated chromosome per machine cycle. High performance is obtained by implementing the functions of parent selection, crossover, mutation, evaluation, and survival in hardware in such a manner that each function can be executed in a single machine cycle. When these hardware functions are connected in a linear pipeline (much the same as an assembly line), the net result is the generation a new child chromosome on each machine cycle. The key features of the survival-based, steady-state GA are low selection pressure due to random parent selection, steady-state population maintenance, and replacement of randomly discovered, lesser-fit chromosomes by more-fit offspring. A GA machine prototype is also presented, running at 1 MHz and generating one million new chromosomes per second.