The enormous variety of substances which may be added to forage in order to manipulate and improve the ensilage process presents an empirical, combinatorial optimization problem of great complexity. To investigate the utility of genetic algorithms for designing effective silage additive combinations, a series of small-scale proof of principle silage experiments were performed with fresh ryegrass. Having established that significant biochemical changes occur over an ensilage period as short as 2 days, we performed a series of experiments in which we used 50 silage additive combinations (prepared by using eight bacterial and other additives, each of which was added at six different levels, including zero [i.e., no additive]). The decrease in pH, the increase in lactate concentration, and the free amino acid concentration were measured after 2 days and used to calculate a "fitness" value that indicated the quality of the silage (compared to a control silage made without additives). This analysis also included a "cost" element to account for different total additive levels. In the initial experiment additive levels were selected randomly, but subsequently a genetic algorithm program was used to suggest new additive combinations based on the fitness values determined in the preceding experiments. The result was very efficient selection for silages in which large decreases in pH and high levels of lactate occurred along with low levels of free amino acids. During the series of five experiments, each of which comprised 50 treatments, there was a steady increase in the amount of lactate that accumulated; the best treatment combination was that used in the last experiment, which produced 4.6 times more lactate than the untreated silage. The additive combinations that were found to yield the highest fitness values in the final (fifth) experiment were assessed to determine a range of biochemical and microbiological quality parameters during full-term silage fermentation. We found that these combinations compared favorably both with uninoculated silage and with a commercial silage additive. The evolutionary computing methods described here are a convenient and efficient approach for designing silage additives.The ensiling of forage crops in order to obtain winter or buffer feed for ruminant livestock is widely practiced in advanced management systems in temperate regions. The aim is to preserve crops having high moisture contents by encouraging rapid fermentation of water-soluble carbohydrates (WSC) in the crops to lactic acid by epiphytic lactic acid bacteria (LAB), which decreases the pH and inhibits the activities of plant enzymes and pathogenic or spoilage bacteria that could decrease the nutritive value of the silage.Grass is the predominant crop ensiled in Europe, and 50 million tons of grass silage are made each year in the United Kingdom alone (23, 61, 62). As with maize, the main crop ensiled in the United States, high WSC levels and a low buffering capacity in this crop are conducive to rapid acidification by epip...