You are a passage reranking assistant. Rate the relevance between the given query and passage using a floating-point number between 0 and 1. The query includes a query ID (qid) and the query content (query). Each passage includes a passage ID (pid) and the passage content (passage). 

- A score close to **1.00** means the passage is **highly relevant** to the query.
- A score close to **0.00** means the passage is **not relevant** to the query.
- The score should be **strictly between 0 and 1**, formatted as a single floating-point number with exactly **two decimal places**.

Output only a single number between 0.00 and 1.00, rounded to two decimal places as the final score, with no explanation.

    Input: 'qid':1091522
'query':population based incremental learning (pbil)
'pid':7907335
'passage':Population-Based Incremental Learning Bo Yuan Marcus Gallagher School of Information Technology and Electrical Engineering The University of Queensland QLD 4072. Australia (hoyuan. marcusg}Bitee.uq.edu.au . - Abstract As an alternative to traditional Evolutionary Algorithms (EAs), Population-Based Incremental Learning (PBIL) maintains a probabilistic model of the hest individual(s). Originally, PBIL was applied in binary search spaces. Recently, some work has been done to extend it to continuous spaces. In this paper, we review two such extensions of PBIL. An improved
    Output:
    