Optimal subdivision of land in agrarian reform projects: an analysis using genetic algorithms

Abstract

Moreira. 2011. Optimal subdivision of land in agrarian reform projects: an analysis using genetic algorithms. Cien. Inv. Agr. 38(2): 169-178. The objective of this manuscript is to develop a new procedure to achieve optimal land subdivision using genetic algorithms (GA). The genetic algorithm was tested in the rural settlement of Veredas, located in Minas Gerais, Brazil. This implementation was based on the land aptitude and its productivity index. The sequence of tests in the study was carried out in two areas with eight different agricultural aptitude classes, including one area of 391.88 ha subdivided into 12 lots and another of 404.1763 ha subdivided into 14 lots. The effectiveness of the method was measured using the shunting line standard value of a parceled area lot's productivity index. To evaluate each parameter, a sequence of 15 calculations was performed to record the best individual fitness average (MMI) found for each parameter variation. The best parameter combination found in testing and used to generate the new parceling with the GA was the following: 320 as the generation number, a population of 40 individuals, 0.8 mutation tax, and a 0.3 renewal tax. The solution generated rather homogeneous lots in terms of productive capacity.

Territorial planning support tool for sustainable forest management in the Amazon

“…In this study, we evaluated the efficiency of SOTER-PA in the territorial planning process of designing Work Units more homogeneous in forest productive capacity, in terms of wood volume and income, than the current management of native forest projects. The use of SOTER-PA can enable a process of sustainable use of natural resources with higher income and more rational use of the stock of native forests, as shown in the studies by Ferreira Neto (2011), Moreira et al (2011), and Ferreira (2015.…”

A population-based iterated greedy algorithm for the delimitation and zoning of rural settlements

“…Genetic algorithms are a type of heuristic search algorithms that are commonly applied to spatial planning for solving land use allocation problems (e.g. Balling et al, 1999;Cao et al, 2012;Stewart et al, 2004;Xin and Zhi-xia, 2008;Ferreira-Neto et al, 2011;Porta et al, 2013). They are based on the principles of natural evolution and use operators as selection, crossover and mutation, and the survival of fitness evolutionary analogy.…”