Illinois Genetic Algorithms Laboratory

Sastry, K., Goldberg, D.E. (2000)
TR No.: 2000026 | Download PDF | Download PS

Abstract:
In this study we present a detailed analysis of the extended compact
genetic algorithm (ECGA). Based on the analysis, empirical relations for
population sizing and convergence time have been derived and are compared
with the existing relations. We then apply ECGA to a non-azeotropic binary
working fluid power cycle optimization problem. The optimal power cycle
obtained improved the cycle efficiency by 2.5% over that existing cycles,
thus illustrating the capabilities of ECGA in solving real-world problems.