Abstract:
Genetic Algorithms work iteratively from generation
to generation to find the optimal solution of optimization
problems. However, due to the probabilistic operations of
Genetic Algorithms (GAs), the performance of GAs search is
unpredictable. Even worst, GAs may not be able to find the
optimal solution after very long iteration. We propose a solution
that incorporates a human intervention to guide GA achieving
a better performance. We adopt the Viral Trait Spreading
Framework for human intervention in the GA operations. Firstly,
we classify GAs operation and then put each group of operation
in the Framework. Most of all genetic algorithm operations fall
into the Trait Adoption component. We optimized the design
of genetic representation and genetic operators to tackle the
fixed element constraint and row permutation constraint of
Sudoku puzzle. Then, we implemented our approach in netlogo,
a multiagent programmable modeling environment. Experiment
results showed that GA is capable of finding the optimal solution
and the human intervention through Viral Trait Spreading
Framework guides the GA in searching processes in the narrower
search space.
