A genetic programming approach to solving optimization problems on agent-based models | Posted on:2017-05-23 | Degree:M.S | Type:Thesis | University:Duquesne University | Candidate:Garuccio, Anthony | Full Text:PDF | GTID:2448390005976258 | Subject:Mathematics | Abstract/Summary: | | 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. | Keywords/Search Tags: | Optimization, Approach, ABM | | Related items |
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