Do not Choose Representation just Change: An Experimental Study in States based EA

TL;DR

This paper investigates how changing coding conversions within a states-based evolutionary algorithm can significantly impact performance, emphasizing the importance of representation transition strategies over static representation choice.

Contribution

It demonstrates that the method of switching between representations during search is crucial for improving evolutionary algorithm efficiency, beyond just selecting the best static representation.

Findings

Changing coding conversions improves EA performance.

Representation transition strategies outperform static representations.

Different conversion procedures significantly affect search outcomes.

Abstract

Our aim in this paper is to analyse the phenotypic effects (evolvability) of diverse coding conversion operators in an instance of the states based evolutionary algorithm (SEA). Since the representation of solutions or the selection of the best encoding during the optimization process has been proved to be very important for the efficiency of evolutionary algorithms (EAs), we will discuss a strategy of coupling more than one representation and different procedures of conversion from one coding to another during the search. Elsewhere, some EAs try to use multiple representations (SM-GA, SEA, etc.) in intention to benefit from the characteristics of each of them. In spite of those results, this paper shows that the change of the representation is also a crucial approach to take into consideration while attempting to increase the performances of such EAs. As a demonstrative example, we use a two states SEA (2-SEA) which has two identical search spaces but different coding conversion operators. The results show that the way of changing from one coding to another and not only the choice of the best representation nor the representation itself is very advantageous and must be taken into account in order to well-desing and improve EAs execution.