Controlling an Exploration in Unbounded Search Space by Novelty-Based Multi-Objectivization

In the "unbounded" search space, an exploration in the search space is more significant issue than that in the "bounded" search space which assumes the pre-defined bounded search space. This is because new areas which have not been searched yet are infinitely found during an exploration in the search space. This means that it is quite important carefully to control such exploration in the unbounded search space to search the targets (e.g., optimal solutions) effectively. However, it is difficult to control exploration in unbounded search space. For this purpose, this paper proposed Novelty-based Multi-objectivization with Local and Role-based Rough area Search (NM-LR2S) which control exploitation, not exploration by adding the mechanisms selecting or generating a good fitness individuals to the conventional method Novelty-based Multi-objectivization with Local and Rough area Search (NM-LRS) and indirectly control exploration. To investigate the effectiveness of the proposed method, the experiment is conducted with NM-LRS which is without exploration controlling mechanisms, and has revealed the following implications: (i) the peak ratio which is the ratio of finding optimal solution of NM-LR2S is better than other unbounded search space optimization methods include NM-LRS; and (ii) NM-LR2S can control the exploration to prevent over-exploration compared with NM-LRS.