A Genetic Programming Approach for Heuristic Selection in Constrained Project Scheduling
Created by W.Langdon from
gpbibliography.bib Revision:1.7630
 @InCollection{padman:1997:GPhscps,

author = "Rema Padman and Stephen F. Roehrig",

title = "A Genetic Programming Approach for Heuristic Selection
in Constrained Project Scheduling",

booktitle = "Interfaces in Computer Science and Operations
Research: Advances in Metaheuristics, Optimization, and
Stochastic Modeling Technologies",

publisher = "Kluwer Academic Publishers",

year = "1997",

editor = "Richard S. Barr and Richard V. Helgason and
Jeffrey L. Kennington",

chapter = "18",

pages = "405421",

address = "Norwell, MA, USA",

keywords = "genetic algorithms, genetic programming",

isbn13 = "9780792398448",

URL = "http://www.amazon.co.uk/InterfacesComputerScienceOperationsResearch/dp/0792398440",

DOI = "doi:10.1007/9781461541028_18",

abstract = "The resourceconstrained project scheduling problem
(RCPSP) with cash flows investigates the scheduling of
activities that are linked by precedence constraints
and multiple resource restrictions. Given the presence
of cash flows which represent expenses for initiating
activities and payments for completed work, maximizing
the Net Present Value of the project is a practical
problem. This is a complex combinatorial optimization
problem which precludes the development of optimal
schedules for large projects. Many heuristics exist for
the RCPSP, but it has proven difficult to decide in
advance which heuristic will provide the best result,
given a problem characterization in terms of parameters
such as size and complexity. In this paper we discuss
the use of genetic programming (GP) for heuristic
selection, and compare it directly to alternative
methods such as OLS regression and neural networks. The
study indicates that the GP approach yields results
which are an improvement on earlier methods. The GP
solution also gives valuable information about project
environments where a given heuristic is inappropriate.
In addition, this approach has no problem evolving
complex nonlinear functions to capture the relationship
between problem parameters and heuristic performance.
Thus the results given in this paper shed light on the
logical domains of applicability of the various
heuristics, while at the same time provide an improved
heuristic selection process.",

notes = "GP used to select which of 16 predefined schduling
heuristic to use. Test case 1440 randomly generated
project networks of 144 types chosen to span a domain.
GP better than ANN approach. cf
\cite{padman:1995:GPhscps}
",
 }
Genetic Programming entries for
Rema Padman
Stephen F Roehrig
Citations