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The Differential Evolution (DE) algorithm goes back to the class of Evolutionary Algorithms and inherits its philosophy and concept. Possessing only three control parameters (size of population, differentiation and recombination constants) DE has promising characteristics of robustness and convergence. In this paper we introduce a new principle of Energetic Selection. It consists in both decreasing the population size and the computation efforts according to an energetic barrier function which depends on the number of generation. The value of this function acts as an energetic filter, through which can pass only individuals with lower fitness. Furthermore, this approach allows us to initialize a population of a sufficient (large) size. This method leads us to an improvement of algorithm convergence.

Calibration population design for genomic prediction has attracted a lot of interest in the plant and animal breeding literature. In this article we present an efficient optimization method to select a subset of preexisting individuals to phenotype. Application to the choice of maize hybrids to create and phenotype, to best predict the unobserved hybrid combination, is demonstrated using real data and simulations. Further, the proposed method is extended to optimize the choice of a connected population design before crosses are actually made. Population design is optimized to maximize efficiency of recurrent selection with genomic prediction. Validation results using real data and simulations are presented.

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When setting up field experiments, to test and compare a range of genotypes (e.g. maize hybrids), it is important to account for any possible field effect that may otherwise bias performance estimates of genotypes. To do so, we propose a model-based method aimed at optimizing the allocation of the tested genotypes and checks between fields and placement within field, according to their kinship. This task can be formulated as a combinatorial permutation-based problem. We used Differential Evolution concept to solve this problem. We then present results of optimal strategies for between-field and within-field placements of genotypes and compare them to existing optimization strategies, both in terms of convergence time and result quality. The new algorithm gives promising results in terms of convergence and search space exploration.