A Heuristic Method to Generate Better Initial Population for Evolutionary Methods
This is an incremental improvement for researchers and practitioners using evolutionary algorithms like GA to optimize performance.
The paper tackles the problem of improving evolutionary algorithms by developing a heuristic method to generate better initial populations, which reduces computation time and improves final solution quality, with efficiency demonstrated through benchmark tests.
Initial population plays an important role in heuristic algorithms such as GA as it help to decrease the time those algorithms need to achieve an acceptable result. Furthermore, it may influence the quality of the final answer given by evolutionary algorithms. In this paper, we shall introduce a heuristic method to generate a target based initial population which possess two mentioned characteristics. The efficiency of the proposed method has been shown by presenting the results of our tests on the benchmarks.