Abstract:
Combination of Fuzzy logic and Genetic
Algorithm is used for searching of optimal solutions.
To obtain the globally optimal rules, the Fuzzy rulebases
are searched by means of Genetic Algorithm.
In Fuzzy inference mechanism, GA is used to find the
optimal rules. Defuzzified the rules from inference
and then the optimal result is produced. This paper
introduced the main concept of Fuzzy Logic, Genetic
Algorithm and the use of GA and Fuzzy Logic. The
input values entered by user are fuzzified with
Triangular or Trapezoidal membership function.
These memberships’ values are used to find the active
rules. These active rules are optimized by GA with
various population sizes. Then the optimal rule is
defuzzified with Weight Average Method and then the
results are described as number of workers and
working days. Genetic Algorithms mimic the process
of natural selection, creating a number of potentially
optimal solutions to some complex search problem.
Computer simulations demonstrate that the fuzzy
logic system with genetic algorithms processes good
robustness.
