QU-GENE has been developed with the flexibility to consider multiple traits. Where linkage maps are constructed through molecular marker investigations of quantitative traits, the linkage map can be specified as the genetic model within the engine of QU-GENE, with the molecular markers and quantitative trait loci identified. Thus, the relative efficiencies of direct and indirect responses to selection for the traits can be examined in the application modules. Scenarios for incorporation of marker assisted selection, or other forms of indirect selection, into breeding programs can then be investigated. Efficient strategies for implementing marker assisted selection options in all relevant application modules is being investigated.
GAs are search algorithms based on the mechanics underlying natural selection.
With the arrival of faster and more efficient computers, the literature on GAs has increased. A problem with many of the genetic algorithms available is that they are prone to climbing local optima on the system response surface being explored and getting stuck on these local optima. Plant breeding strategies, other than mass selection, that retain greater quantities of genetic variation within the populations of genotypes that are being manipulated may offer alternative search strategies to the currently used genetic algorithms. QU-GENE is being used as a tool to investigate the relative efficiency of breeding strategies in comparison with genetic algorithms as possible search strategies for complex systems with rugged response surfaces. We are currently examining the principles used in these disciplines to explore their fitness landscapes.