# Evolving the Hearthstone Meta

## TL;DR

This paper presents an evolutionary algorithm-based approach to balance Hearthstone cards by analyzing match-up win rates, aiming to achieve fairer gameplay with minimal changes to existing cards.

## Contribution

It introduces a multi-objective evolutionary method to optimize card balancing with minimal disruption, along with heuristics for selecting cards to modify.

## Key findings

- The approach effectively balances decks to near 50% win rates.
- Minimal card changes can significantly impact game balance.
- Heuristics help identify key cards for balancing.

## Abstract

Balancing an ever growing strategic game of high complexity, such as Hearthstone is a complex task. The target of making strategies diverse and customizable results in a delicate intricate system. Tuning over 2000 cards to generate the desired outcome without disrupting the existing environment becomes a laborious challenge. In this paper, we discuss the impacts that changes to existing cards can have on strategy in Hearthstone. By analyzing the win rate on match-ups across different decks, being played by different strategies, we propose to compare their performance before and after changes are made to improve or worsen different cards. Then, using an evolutionary algorithm, we search for a combination of changes to the card attributes that cause the decks to approach equal, 50% win rates. We then expand our evolutionary algorithm to a multi-objective solution to search for this result, while making the minimum amount of changes, and as a consequence disruption, to the existing cards. Lastly, we propose and evaluate metrics to serve as heuristics with which to decide which cards to target with balance changes.