Observations on exploration and exploitation effects on solving Rubik's cube using evolutionary strategies

Rubik's cube have been a popular problem in Evolutionary Computations (EC) for its huge search space. In this paper we present an Evolutionary Strategy (ES) approach that searches for a sequence of moves to solve a scrambled cube. We applied different population size and number of parents to study the effect of exploration and exploitation rates on ES performance using multiple (μ, λ) selection configurations. We also studied the cube's scrambleness effect on the performance.