A computational approach to animal breeding.
We propose a computational model of mating strategies for controlled animal breeding programs. A mating strategy in a controlled breeding program is a heuristic with some optimization criteria as a goal. Thus, it is appropriate to use the computational tools available for analysis of optimization heuristics. In this paper, we propose the first discrete model of the controlled animal breeding problem and analyse heuristics for two possible objectives: (1) breeding for maximum diversity and (2) breeding a target individual. These two goals are representative of conservation biology and agricultural livestock management, respectively. We evaluate several mating strategies and provide upper and lower bounds for the expected number of matings. While the population parameters may vary and can change the actual number of matings for a particular strategy, the order of magnitude of the number of expected matings and the relative competitiveness of the mating heuristics remains the same. Thus, our simple discrete model of the animal breeding problem provides a novel viable and robust approach to designing and comparing breeding strategies in captive populations.
Berger-Wolf
Tanya Y
TY
Department of Computer Science, University of Illinois at Chicago, 851 S. Morgan St, Chicago, IL 60607, USA. tanyabw@uic.edu
Moore
Cristopher
C
Saia
Jared
J
D000465:Algorithms; D000822:Animal Husbandry; D000818:Animals; D001947:Breeding; D019295:Computational Biology; D003198:Computer Simulation; D003247:Conservation of Natural Resources; D014644:Genetic Variation; D011156:Population Density