February 22, 2017

An Artificial Intelligence Approach to VLSI Design by Thaddeus J. Kowalski

By Thaddeus J. Kowalski

Ebook via Kowalski, Thaddeus J.

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H. ) Intelligent Engineering Systems Through Artificial Neural Networks, vol. 14, pp. 223–228. ASME Press (2004) 3. : Genetic programming for pedestrians. In: S. ) Proc. International Conference on Genetic Algorithms, p. 628. Morgan Kaufmann (1993) 4. : Genetic Programming for pedestrians. Tech. Rep. 93-03, Mitsubishi Electric Research Labs, Cambridge, MA (1993) 5. : Genetic Programming: An Introduction. Morgan Kaufmann (1999) 6. : A Comparison of Linear Genetic Programming and Neural Networks in Medical Data Mining.

4. Given an expression, with a number of production rules N and a codon C, a rewriting rule is chosen according to R = C mod N. 0 ::= ::=() ::=sin ::= ::=x ::= * ::= ::=x ::= * ::=() ::=sin ::= ::=x Since, in GE, genotypes are binary strings, no special crossover or mutation operators are required. The genotype-to-phenotype mapping process will always generate syntactically correct individuals.

Push handles multiple data types by providing a stack for each type. There is a stack for integers, a stack for floating-point numbers, a stack for Boolean values, a stack for data types (called TYPE), a stack for program code (CODE) and others. Each instruction takes the inputs that it requires from appropriate stacks and pushes outputs onto appropriate stacks. Using the CODE stack allows Push to handle recursion and subprocedures. The CODE stack also allows evolved programs to push themselves or parts of themselves onto the CODE stack.

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