We study an approach of integrated genetic learning in construction of fuzzy relational architectures (described by fuzzy relational equations). These equations have been widely utilized e.g., in fuzzy model identification, fuzzy control, and fuzzy controllers. While the theoretical foundations of the equations are well developed, they still call for more efficient and diversified schemes of learning. The paper addresses this issue by synergistically combining some fundamental concepts of genetic algorithm (GA) and standard gradient-based techniques into a unified scheme of stratified learning. Especially, we reveal how the ideas of GAs can be effectively used at the level of the initialization of the gradient-based learning schemes. It is also clarified how an optimal subpopulation of the strings can be used in forming feasibility regions (guarding zones), useful in supervision of the second phase of parametric learning.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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