Constrained Modified Genetic Algorithm for Optimizing RICE Climate Change Model Policy

Abstract

The objective of this paper is to use evolutionary algorithm for policy making to help in decision support, the Regional Integrated Climate-Economy (RICE) model for the dynamic climate change is used to optimize the tradeoff policy between abating of carbon dioxide emissions to reduce global climate change and in the other hand the resulting in economic damages. A Constrained Genetic Algorithms (CGAs) is modified to search for near global optimal solutions the by searching climate optimum control parameters that resulted in optimal CO₂ abatement and temperature reduction with less economic damages. A Comparison study between optimizing the output of GAs with the standard solution revealed that GAs successfully found a better solution, in term of finding optimum values for the carbon prices that lead to more reduction in carbon emission comparing to solutions given by the model developer.