The acreage planted in corn and soybean crops is vast, and these crops contribute substantially to the world economy. The agricultural practices employed for farming these crops have major effects on ecosystem health at a worldwide scale. The microbial communities living in agricultural soils significantly contribute to nutrient uptake and cycling and can have both positive and negative impacts on the crops growing with them. In this study, we examined the impact of the crop planted and soil tillage on nutrient levels, microbial communities, and the biochemical pathways present in the soil. We found that farming practice, that is conventional tillage versus no‐till, had a much greater impact on nearly everything measured compared to the crop planted. No‐till fields tended to have higher nutrient levels and distinct microbial communities. Moreover, no‐till fields had more DNA sequences associated with key nitrogen cycle processes, suggesting that the microbial communities were more active in cycling nitrogen. Our results indicate that tilling of agricultural soil may magnify the degree of nutrient waste and runoff by altering nutrient cycles through changes to microbial communities. Currently, a minority of acreage is maintained without tillage despite clear benefits to soil nutrient levels, and a decrease in nutrient runoff—both of which have ecosystem‐level effects and both direct and indirect effects on humans and other organisms.

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Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

A genetic ,'dgorithm is used to select the inputs to A neural network function ApproximAtor. lit the application considered, modeling criticM parameters of the Space Shuttle Main Engine (SSME), the functional rel,_tionslfip between mea._ured parameters is unknown and coxuplex. Furthermore, the number of possible input parameters is quite large. MAlty approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due the optimization altd space searching capabities of genetic Mgorithms they were employed in this paper to systematize the input selection process. The results suggest that the genetic Mgorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.