Survival of the fittest—genetic algorithms versus evolution strategies in the optimization of systems models

Abstract

The use of numerical optimization techniques on simulation models is a developing field. Many of the available algorithms are not well suited to the types of problems posed by models of agricultural systems. Coming from different historical and developmental backgrounds, both genetic algorithms and evolution strategies have proven to be thorough and efficient methods in identifying the global optimum of such systems. A challenging herd dynamics model is used to test and compare optimizations using binary and real-value genetic algorithms, as well as evolution strategies. All proved successful in identifying the global optimum of this model, but evolution strategies were notably slower in achieving this. As the more successful innovations of each of these methods are being commonly adopted by all, the boundaries between them are becoming less clear-cut. They are effectively merging into one general class of optimization methods now termed evolutionary algorithms.