The study focuses on the use of genetic algorithms in complex 
and coupled multi-disciplinary design problems. Two special 
classes of problems are identified for study; these include 
topology design problems and situations in which a large 
coupled problem must be solved as a sequence of smaller 
subproblems. The latter is referred to as a decomposition 
based design strategy. Binary coded genetic algorithms have 
been used effectively in the topological design of discrete 
structural systems. In a majority of such applications, the 
structural topology is extracted from a pre-defined "structural 
universe", a set of all permissible joints and elements that 
can be used in the development of the optimal design. In the 
presence of a dense "structural universe", the genetic 
algorithm search process must contend with very long string 
lengths, with attendant degradation in the effectiveness of 
the search process. The present study presents a novel approach 
for handling binary strings that may have variable string 
lengths and/or varying binary string representations of design 
variables. Varying string lengths in a population and topological
difference of string representation requires a redefinition of 
the crossover process, and both inter- and intra-species 
crossover mechanisms are explored in this study. The use of 
micro genetic algorithms is proposed as an approach to increase 
the search efficiency in problems involving a large number of 
candidate topologies. The proposed strategies are implemented 
in representative algebraic problems, truss topology design, 
and the layout of a stiffened composite panel. New strategies 
for the implementation of genetic algorithms in a decomposition 
based design optimization were examined in this study. In 
decomposition based approaches, the design problem is decomposed 
into smaller-sized sub problems, the solutions for which are 
obtained through co-evolution. The emphasis resides in evaluating 
methods for exchanging design information relevant to coordinating 
solutions of temporarily decoupled sub problems. Methods based 
on modification of genetic makeup through experiential inheritance 
(exposure to another species), and through inter species migration 
are investigated in this work. Different forms of design problem 
coupling are investigated, ranging from coupling through constraints 
only, to coupled objective and constraint functions. The proposed
strategies are validated through implementation in representative 
algebraic and structural design problems. The strategies for 
decomposition based design optimization were extended to multicriteria
design optimization environment. Among many methods to address 
multicriteria optimization problems, the weighted sum and the 
weighted minimax method were used to investigate that experiential 
inheritance and interspecies migration is applicable to multicriteria 
optimization problems. The proposed strategies are tested in 
representative algebraic problems, a truss design, and a stiffened 
composite panel design.