Genetic algorithms (GA) are adaptive search techniques
that derive their models from the genetic processes of bio-
logical organisms based on evolution theory. The interest on
GAs is rising fast, for they provide a robust and powerful
adaptive search mechanism. The most important advantage
of GAs is that they use only the pay off (objective function)
information and hence independent of the nature of the
search space such as smoothness, convexity or unimodality.
GAs are increasingly applied in solving Power System Opti-
mization problems in recent years ([1-4]). A literature
survey shows that GAs have been successfully applied to
solve economic load dispatch (ELD) problem [5-11].
However, none of these works has considered the Lineflow
constraints, which are so important for any practical imple-
mentation of ELD. The present work solves the ELD
problem with Lineflow constraints through effective appli-
cation of GA, considering the system transmission losses,
power balance equation as the equality constraint, limits on
the active power generations of the units and limits on
currents in different lines (Lineflow constraints) as the
inequality constraints. Two test systems, i.e. IEEE 14 bus
[16] and IEEE 30 bus [16] systems have been considered for
the investigations. The ELD results with GA have been
compared with those obtained through Classical technique
GA [5] differs from Classical optimization techniques in
that it works on a population of solutions and searching is on
a bit string encoding of the real parameters rather than
the parameters themselves. Also GA uses probabilistic tran-
sition rules. Each string in the population representing a
possible solution is made up of a number of sub-strings.
The algorithm starts from an initial population generated
randomly. This population undergoes three genetic opera-
tions, Selection, Crossover and Mutation to produce a new
generation after duly considering the fitness of strings,
which corresponds to the objective function for the
concerned problem. A trial solution for the problem requires
the selection of a number of populations for a generation
and a number for several such generations in order to find
the best fitness of strings (best objective function) in that
trial. Several such trials are considered to evaluate the over-
all best objective function. The best value of the fitness of
the strings is dependent on the number of population in a
generation, the number of generations and the number of
trials while solving the problem through GA.