A genetic algorithm approach to policy design for consequence minimization

Abstract We characterize the policy design problem as an event network where a series of interrelated decisions are made in a sequential fashion. As such, each node represents a decision point and determines the next arc to be connected in the network. A key objective in designing new policies is the minimization of negative outputs (consequence minimization). To address this type of problem we have applied a genetic algorithm (GA) to generate multiple network configurations (policy alternatives) for evaluation by human decision-makers. Our approach differs from typical genetic algorithms because decision-maker participation has been intimately linked to the genetic search so that policies designed will simultaneously meet the objectives for which they have been designed and remain acceptable and implementable in practice. In this paper we construct a solid waste management example to illustrate the usefulness of our approach to policy design.

[1]  Lawrence Davis,et al.  Job Shop Scheduling with Genetic Algorithms , 1985, ICGA.

[2]  I Zandi,et al.  Application of a Genetic Algorithm to Policy Planning: The Case of Solid Waste , 1999 .

[3]  G. Anandalingam,et al.  Genetic algorithm based approach to bi-level linear programming , 1994 .

[4]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[5]  David E. Goldberg,et al.  Genetic Algorithms and Rules Learning in Dynamic System Control , 1985, ICGA.

[6]  D. J. Cavicchio,et al.  Adaptive search using simulated evolution , 1970 .

[7]  Douglass B. Lee.  Requiem for Large-Scale Models , 1973 .

[8]  Lawrence Davis,et al.  Genetic Algorithms and Communication Link Speed Design: Theoretical Considerations , 1987, ICGA.

[9]  David E. Goldberg,et al.  Alleles, loci and the traveling salesman problem , 1985 .

[10]  T. Saaty,et al.  The Analytic Hierarchy Process , 1985 .

[11]  Yoshikazu Fukuyama,et al.  A PARALLEL GENETIC ALGORITHM F GENERATION EXPANSION PLANNIN , 1996 .

[12]  Saul I. Gass,et al.  Feature Article - Decision-Aiding Models: Validation, Assessment, and Related Issues for Policy Analysis , 1983, Oper. Res..

[13]  Lawrence Davis,et al.  Applying Adaptive Algorithms to Epistatic Domains , 1985, IJCAI.

[14]  Franklin Allen,et al.  Using genetic algorithms to find technical trading rules , 1999 .

[15]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[16]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[17]  Jim R. Oliver.  On artificial agents for negotiation in electronic commerce , 1996, Proceedings of HICSS-29: 29th Hawaii International Conference on System Sciences.

[18]  David E. Goldberg,et al.  Dynamic System Control Using Rule Learning and Genetic Algorithms , 1985, IJCAI.

[19]  Godwin J. Udo,et al.  Factors affecting decision support system benefits , 1992, Inf. Manag..