Motivation and framework for using genetic algorithms for microcode compaction

Authors:
Steven Beaty;Darrell Whitley;Gearold Johnson
Affiliations:
Department of Mechanical Engineering, Colorado State University, Fort Collins, Colorado;Department of Computer Science, Colorado State University, Fort Collins, Colorado;Department of Mechanical Engineering, Colorado State University, Fort Collins, Colorado
Venue:
MICRO 23 Proceedings of the 23rd annual workshop and symposium on Microprogramming and microarchitecture
Year:
1990

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Abstract

Genetic algorithms are a robust adaptive optimization technique based on a biological paradigm. They perform efficient search on poorly-defined spaces by maintaining an ordered pool of strings that represent regions in the search space. New strings are produced from existing strings using the genetic-based operators of recombination and mutation. Combining these operators with natural selection results in the efficient use of hyperplane information found in the problem to guide the search. The searches are not greatly influenced by local optima or non-continuous functions. Genetic algorithms have been successfully used in problems such as the traveling salesperson and scheduling job shops. Microcode compaction can be modeled as these same types of problems, which motivates the application of genetic algorithms in this domain.