  A Genetic Algorithm (GA) is proposed in which each member of the population
can change schemata only with its neighbors according to a rule. The rule
methodology and the neighborhood structure employ elements from the Cellular
Automata (CA) strategies. Each member of the GA population is assigned to a
cell and crossover takes place only between adjacent cells, according to the
predefined rule. Although combinations of CA and GA approaches have appeared
previously, here we rely on the inherent self-organizing features of CA, rather
than on parallelism. This conceptual shift directs us toward the evolution of
compact populations containing only a handful of members. We find that the
resulting algorithm can search the design space more efficiently than
traditional GA strategies due to its ability to exploit mutations within this
compact self-organizing population. Consequently, premature convergence is
avoided and the final results often are more accurate. In order to reinforce
the superior mutation capability, a re-initialization strategy also is
implemented. Ten test functions and two benchmark structural engineering truss
design problems are examined in order to demonstrate the performance of the
method.
