构建模块化跨领域遗传编程评测框架，统一比较不同表示方法在多任务中的表现。
TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming
- 设计模块化框架，支持多种解的表示方式和问题域
- 涵盖符号回归、逻辑综合与策略搜索三大任务领域
- 为遗传编程提供可比、可扩展的跨领域评测标准
近年来，遗传编程（GP）不断发展，涌现出多种解决方案表示形式。作为本质上的程序合成算法，它能应对多个问题领域。然而当前的评测体系较为分散，不同表示方式之间缺乏直接比较，且性能未在多领域间进行系统评估。本文提出一个统一框架 TinyverseGP（受 tinyGP 启发），支持多种表示方式和问题领域，包括符号回归、逻辑综合与策略搜索，旨在建立一个可扩展、可比的跨领域评测基准。
原文摘要 · Abstract (English)
Over the years, genetic programming (GP) has evolved, with many proposed variations, especially in how they represent a solution. Being essentially a program synthesis algorithm, it is capable of tackling multiple problem domains. Current benchmarking initiatives are fragmented, as the different representations are not compared with each other and their performance is not measured across the different domains. In this work, we propose a unified framework, dubbed TinyverseGP (inspired by tinyGP), which provides support to multiple representations and problem domains, including symbolic regression, logic synthesis and policy search.
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