Placing Text Labels on Maps and Diagrams using Genetic Algorithms with Masking

Abstract

Cartographic label placement is one of the most time-consuming tasks in the production of high quality maps and other high quality graphical displays. It is essential that text labels used to identify various features and objects be placed in a clear and unobscured manner. In this article we are concerned with the placement of labels for point features. Specifically, the point feature label placement (PFLP) problem is the problem of placing text labels to point features on a map, graph, or diagram in such a manner so as to maximize legibility. The PFLP problem has been shown to be NP-hard. We propose a heuristic method for the PFLP problem based on genetic algorithms (GA), an adaptive, robust, search and optimization technique based on the principles of natural genetics and survival of the fittest. In particular we emphasize the notion of masking to preserve optimal subsequences in chromosomes and prevent their disruption during crossover and mutation. We ran our algorithms on randomly placed point features in a region, and on datasets from various regions of the USA map with great success. Our GA Implementation with masking solved each of the test cases extremely well, and proved to be an excellent heuristic for solving the PFLP problem. Furthermore, our GA with masking performed significantly better than other PFLP algorithms from the literature.