Genetic Algorithms (GAs) are powerful tools to solve large scale design optimization problems. The research interests in GAs lie in both its theory and application. On one hand, various modifications have been made on early GAs to allow them to solve problems faster, more accurately and more reliably. On the other hand, GA is used to solve complicated design optimization problems in different a...

Reproduction is a key profit driver in beef cattle production systems around the world. However genetic evaluation for reproduction traits is generally limited or non-existent. This is likely a function of low levels of recording reproduction traits, and low heritabilities. Traditional BLUP based genetic evaluations have been used effectively in beef for generating estimated breeding values and...

Building on a solid foundation of performance testing, quantitative genetics, and statistical estimation of breeding values; recent developments in pig line improvement programs have included the application of both major gene and marker genomics, and an increased focus on genotype by environment interactions and crossbred selection objectives. Additionally, selection objectives have broadened ...