A Genetic Programming Approach to Solving Optimization Problems on Agent-Based Models

In this thesis, we present a novel approach to solving optimization problems that are defined on agent-based models (ABM). The approach utilizes concepts in genetic programming (GP) and is demonstrated here using an optimization problem on the Sugarscape ABM, a prototype ABM that includes spatial heterogeneity, accumulation of agent resources, and agents with different attributes. The optimization problem seeks a strategy for taxation of agent resources which maximizes total taxes collected while minimizing impact on the agents over a finite time. We demonstrate how our GP approach yields better taxation policies when compared to simple flat taxes and provide reasons why GP-generated taxes perform well. We also look at ways to improve the performance of the GP optimization method.

Name thesis:degree_name

MS

Level thesis:degree_level

Immediate Access

Discipline thesis:degree_discipline

Computational Mathematics

Year dc:date.available

2016

Author dc:creator

• Garuccio, Anthony

Contributors dc:contributor

• Rachael Neilan
• Donald Simon
• John Kern

• Agent Based Modeling
• Genetic Programing
• Optimal Control
• Sugarscape
• Taxation

Language dc:language

English

Repository record dc:identifier

https://dsc.duq.edu/etd/569

OAI identifier oai:identifier

oai:dsc.duq.edu:etd-1585