The genetic algorithms (GA) is one of
the most promising techniques in
solving optimization reservoir
operation and has received a great deal
of attention regarding complex
systems. The GA is essentially a
Darwinian natural selection process
which combines an artificial survival
of the fittest with natural genetic
operators. [6]. It represents a solution
using strings (or chromosomes) of
variables that represents the problem.
Each strings comprises a number of
genes comprised the string depends on
the decision variables of the objective
function. During the generation , the
individuals in the current population
are rated for their effective evolutions
and new population of candidate
solutions is formed using specific
genetic operators such as reproduction,
crossover and mutation, [7].Through the
genetic evolution method, an optimal
solution can be found and represented
by the final winner of genetic
evolution. The GA is an iterative
procedure which maintains a
population of individuals that are
candidates solutions to specific
domain.[1] used GAs to develop
operating polices for multipurpose
reservoir systems leading to effective
solution