Abstract
The present paper discusses a new approach to depicting an evolutionary optimization process. The approach depicts an evolutionary optimization process as change in distribution of degree of nodes in a network that emerges in the process. An evolutionary optimization process involves identification of interactions between variables, and ability in identifying the interactions influences the search efficiency. Therefore, network structures formed by using the indentified interactions information in an evolutionary process draw how an evolutionary algorithm solves a given optimization process. Futhermore, such a way of depicting an evolutionary optimization process could be utilized for classifying evolutionary algorithms. As an example, a mutation-based evolutionary algorithm that evolves developmental timings is used, and it is shown that power-law-like network topologies emerge in its evolutionary optimization process even for uniformly-scaled problems.
