This project was created in the process of writing a paper("Is it worth to approximate fitness function by machine learning")

It is usual to need an approximate model in evolutionary computation when fitness function is deemed to be abstract or expected to have a long computation time. In these cases, research on possibility of fitness approximation should proceed before applying an evolutionary algorithm in real-world problems. In this paper, it was found that we could train machine learning algorithms with the sampled solutions when problem size is large, if there is a possibility of fitness approximation at small problem sizes.

세 가지 기계 학습 알고리즘을 사용해서 모델을 만들고, 해당 모델로 적합도를 근사

• DNN의 경우 Tensorflow로 구현(fitness.py)
• LR, SVR의 경우 WEKA로 구현(fitness.java)

gcc -o GA GA-Problems.c
./GA nk 20 temp RANDOM 2
# nk - type of problem
# temp - File name, It has best solutions's fitness
# 2 - NK_K
# 20 - NK_N