In standard evolutionary algorithms, new individuals are generated by random mutation and recombination. In Eugenic Evolution, individuals are systematically constructed to maximize fitness, based on historical data on correlations between allele and fitness values. This method, Eugenic Algorithm (EuA), compares favorably to standard methods such as Simulated Annealing and Genetic Algorithms in general combinatorial optimization tasks. The Eugenic principle has also been applied to the evolution of neural networks in a method called EuSANE, where new networks are systematically constructed from a pool of candidate neurons. The EuA principle is further enhanced in the TEAM method, where statistical models for each gene are individually maintained.