Genetic Programming involves the evolution of computer programs, which are usually represented by trees composed by functions and terminals. In order to assign fitness, one must evaluate the programs, which is the most time demanding step of GP. In nowadays standard approaches, the evaluation involves an interpretation step. To avoid this step, which significantly slows the algorithm, some researchers evolve, directly, machine code programs. An alternative approach is to build a Genome Compiler, i.e. a system that transforms the individual’s trees in machine-code programs and executes this code. Both techniques can bring huge speed improvements. However, these approaches have some shortcomings. In this paper we present GenCo: a research project whose main goal is development of a Genetic Programming Genome Compiler system, that overcomes some of the drawbacks of current approaches, enabling high speed improvements in a wider range o! f domains. We will also present experimental results in a programmatic compression task, in which GenCo was, on average, 80 times faster than a standard C based GP system.
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References in zbMATH (referenced in 3 articles , 1 standard article )
Showing results 1 to 3 of 3.
- Sen, Suvrajeet; Higle, Julia L.: The $C^3$ theorem and a $D^2$ algorithm for large scale stochastic mixed-integer programming: set convexification (2005)
- Yang, Weiguo; Sheblé, Gerald B.: Discrete generation decisions simulation including market dynamic interactions (2003)
- Machado, Penousal; Dias, André; Cardoso, Amílcar: GenCo: a project report (2001)