The second volume of two examining genetic algorithms (GA) as a means to assess or develop systems by applying the rules of reproduction, gene crossover, and mutation. Fourteen essays written by distinguished researchers examine the potentiality of GA in artificial neural network evolution, parameter estimation, the Boltzmann selection procedure, hybrid approaches for real-time sequencing and scheduling problems, and chemical engineering. The discussions use tables and graphs to help illustrate key elements.