# A comparison of evaluation methods in coevolution

## TL;DR

This paper compares four evaluation methods in coevolution using the Majority Function problem, finding that combining average score and weighted informativeness yields more accurate assessments.

## Contribution

It introduces a comparison framework for evaluation methods in coevolution and demonstrates the effectiveness of combining specific measures for better evaluation accuracy.

## Key findings

- Weighted informativeness improves evaluation accuracy
- Combination of average score and weighted informativeness is more reliable
- OF C is computed on a real problem for the first time

## Abstract

In this research, we compare four different evaluation methods in coevolution on the Majority Function problem. The size of the problem is selected such that evaluation against all possible test cases is feasible. Two measures are used for the comparisons, i.e., the objective fitness derived from evaluating solutions against all test cases, and the objective fitness correlation (OFC), which is defined as the correlation coefficient between subjective and objective fitness. The results of our experiments suggest that a combination of average score and weighted informativeness may provide a more accurate evaluation in coevolution. In order to confirm this difference, a series of t-tests on the preference between each pair of the evaluation methods is performed. The resulting significance is affirmative, and the tests for two quality measures show similar preference on four evaluation methods. %This study is the first time OFC is actually computed on a real problem. Experiments on Majority Function problems with larger sizes and Parity problems are in progress, and their results will be added in the final version.