Conference Paper

Bio-inspired simulation tool for PERT

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This Genetic Programming based tool simulates activities and resource allocations in the Program (Project) Evaluation and Review Technique method of project control. Users constrain the optimization problem by means of a visual interface and Genetic Programming discovers a umber of acceptable solutions that satisfy the user constraints. It evolves computer programs that, when executed, produce a variable length vector of real numbers. This vector is then interpreted according to the grammar that abides by the user constraints. The tool has a wide application in the management of large and complex projects as it handles the a priori simulation of events that may delay or compromise the project, and enables the project owners and project managers to come up with robust and innovative contingency measures to decrease the likelihood of project failure before project start-up.

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... It results in a variable length vector of elements each in the range [0, 1]. For an explanation of the GP functions and terminals and the workings of the GGP see previous publications [6] [5] [4] [8]. ...
Conference Paper
This novel algorithm uses standard Genetic Programming (GP) to evolve a grammar. It is applied to the automatic parallelization of sequential software. Alternative parallel schedules are generated for a computational resource constrained illustrative example demonstrating the power of the methodology.
Conference Paper
A method is presented for evolving individuals that use an Attribute Grammar (AG) in a generative way. AGs are considerably more flexible and powerful than the closed, context free grammars normally employed by GP. Rather than evolving derivation trees as in most approaches, we employ a two step process that first generates a vector of real numbers using standard GP, before using the vector to produce a parse tree. As the parse tree is being produced, the choices in the grammar depend on the attributes being input to the current node of the parse tree. The motivation is automatic parallelization or the discovery of a re-factoring of a sequential code or equivalent parallel code that satisfies certain performance gains when implemented on a target parallel computing platform such as a multicore processor. An illustrative and a computed example demonstrate this methodology.
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