By F J Corbató; J W Poduska; J H Saltzer
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Additional info for Advanced computer programming : a case study of a classroom assembly program
Kluwer, Norwell, MA, USA. Woodward, John R. (2003). Evolving Turing complete representations. , editors, The 2003 Congress on Evolutionary Computation, CEC 2003, Canberra, Australia, 8–12 December, 2003, volume 2, pages 830–837. IEEE Press. Chapter 2 TOWARDS PRACTICAL AUTOCONSTRUCTIVE EVOLUTION: SELF-EVOLUTION OF PROBLEM-SOLVING GENETIC PROGRAMMING SYSTEMS Lee Spector Cognitive Science, Hampshire College, Amherst, MA, 01002-3359 USA. Abstract Most genetic programming systems use hard-coded genetic operators that are applied according to user-specified parameters.
The JVM holds a stack for each execution thread, and creates a frame on this stack for each method invocation. The frame contains a code array, an operand stack, a local variables array, and a reference to the constant pool of the current class (Engel, 1999). The code array contains the bytecode to be executed by the JVM. The local variables array holds all method (or function) parameters, including a reference to the class instance in which the current method executes. In addition, the variables array also holds local-scope variables.
In the next section this earlier work is briefly described. The subsequent section describes more recent work on the approach, using better technology and a more explicit focus on the goal 22 Genetic Programming Theory and Practice VIII of high performance problem solving, implemented in a newer system called AutoPush. The final section of the chapter offers some brief conclusions. 2. ” The goal in the previous work, as in the work described here, is for the ways in which children are produced to be evolved along with the programs to which they will be applied.
Advanced computer programming : a case study of a classroom assembly program by F J Corbató; J W Poduska; J H Saltzer