Approximating a multi-dimensional Pareto front for a land use management problem: A modified MOEA with an epigenetic silencing metaphor

Land use management is increasingly becoming complex as the public and governing bodies demand more accountability and transparency in management practices that simultaneously guarantee sustainable production of goods and continued provision of ecosystem services (i.e., public goods with no markets, such as clean air). In this paper we demonstrate a novel form of decision making that will assist in meeting some of these challenges in ensuring sustainability in land use management. We apply a modified Multi-Objective Evolutionary Algorithm (MOEA), influenced by epigenetic silencing, to a farm case study. The result is a set of time-series, farm management strategies and their related spatial arrangements of land uses that satisfy 14 incommensurable and sometimes conflicting objectives, and spatial constraints. The 14 objectives cover economic (i.e. productivity and financials) and environmental issues. Choosing a single strategy from the set for implementation will require social-ethical value judgment determined from preferences and values of multiple decision-makers. This part of the decision making process is beyond the scope of this paper, but will contribute to ongoing research which will make it possible to fully account for the Triple Bottom Line (TBL), characterised by environmental, economic and social elements.

[1]  Henry Nicholls The uncertainty at the heart of evolution , 2011 .

[2]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[3]  W. Leach Public Involvement in USDA Forest Service Policymaking: A Literature Review , 2006 .

[4]  Bruce Tonn,et al.  STAKEHOLDER INVOLVEMENT: OPEN PROCESSES FOR REACHING DECISIONS ABOUT THE FUTURE USES OF CONTAMINATED SITES FINAL REPORT , 1993 .

[5]  Kalyanmoy Deb,et al.  Multiple Criteria Decision Making, Multiattribute Utility Theory: Recent Accomplishments and What Lies Ahead , 2008, Manag. Sci..

[6]  JR Bryant,et al.  Description and evaluation of the Farmax Dairy Pro decision support model , 2010 .

[7]  Oliver Chikumbo,et al.  Using Different Approaches to Approximate a Pareto Front for a Multiobjective Evolutionary Algorithm: Optimal Thinning Regimes for Eucalyptus fastigata , 2012 .

[8]  Iris Vessey,et al.  Cognitive Fit: A Theory‐Based Analysis of the Graphs Versus Tables Literature* , 1991 .

[9]  Bernard De Baets,et al.  Single versus multiple objective genetic algorithms for solving the even-flow forest management problem , 2004 .

[10]  Z. Bar-Joseph,et al.  Algorithms in nature: the convergence of systems biology and computational thinking , 2011, Molecular systems biology.

[11]  J. Mellor,et al.  Dynamic nucleosomes and gene transcription. , 2006, Trends in genetics : TIG.

[12]  Victor DeMiguel,et al.  Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy? , 2009 .

[13]  Matthew E Ritchie,et al.  High-resolution transcription atlas of the mitotic cell cycle in budding yeast , 2010, Genome Biology.

[14]  B. Linggi,et al.  Translating the histone code into leukemia , 2005, Journal of cellular biochemistry.

[15]  JR Bryanta,et al.  Description and evaluation of the Farmax Dairy Pro decision support model , 2010 .

[16]  Kalyanmoy Deb,et al.  Reference point based multi-objective optimization using evolutionary algorithms , 2006, GECCO.

[17]  Gunar E. Liepins,et al.  Some Guidelines for Genetic Algorithms with Penalty Functions , 1989, ICGA.

[18]  P. Beets,et al.  Description and validation of C_change: A model for simulating carbon content in managed Pinus radiata stands , 1999 .

[19]  Alan D. Christiansen,et al.  An empirical study of evolutionary techniques for multiobjective optimization in engineering design , 1996 .

[20]  Kalyanmoy Deb,et al.  Visualizing multi-dimensional pareto-optimal fronts with a 3D virtual reality system , 2008, 2008 International Multiconference on Computer Science and Information Technology.

[21]  A. Dons,et al.  HYDROLOGY AND SEDIMENT REGIME OF A PASTURE, NATIVE FOREST, AND PINE FOREST CATCHMENT IN THE CENTRAL NORTH ISLAND, NEW ZEALAND , 1987 .

[22]  K. L. Johns,et al.  OVERSEER ® nutrient budgets – moving towards on-farm resource accounting , 2022 .

[23]  S. Maas,et al.  Molecular diversity through RNA editing: a balancing act. , 2010, Trends in genetics : TIG.

[24]  Carlos M. Fonseca,et al.  Multi-objective evolutionary algorithm for land-use management problem , 2007 .

[25]  Martina Maida,et al.  Explaining MCDM acceptance: A conceptual model of influencing factors , 2011, 2011 Federated Conference on Computer Science and Information Systems (FedCSIS).

[26]  O. Chikumbo,et al.  Planning and monitoring forest sustainability: an Australian perspective , 2001 .

[27]  B. J. Turner,et al.  Optimisation modelling of sustainable forest management at the regional level: an Australian example , 2002 .

[28]  Kalyanmoy Deb,et al.  Reference point based multi-objective optimization using evolutionary algorithms , 2006, GECCO '06.

[29]  R. F. Luco,et al.  Epigenetics in Alternative Pre-mRNA Splicing , 2011, Cell.

[30]  W. Banzhaf,et al.  Evolvability and Speed of Evolutionary Algorithms in Light of Recent Developments in Biology , 2010, Journal of Artificial Evolution and Applications.

[31]  R. Benne,et al.  RNA editing in trypanosomes , 1992, Molecular Biology Reports.

[32]  P. Maclaren,et al.  Modelling the effect of land-use practices on greenhouse gas emissions and sinks in New Zealand , 1999 .

[33]  A. Riggs,et al.  Epigenetic Changes and Repositioning Determine the Evolutionary Fate of Duplicated Genes , 2005, Biochemistry (Moscow).