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Automated Design Competition Technical Report: Cascaded Structure and Parameter Optimization Based on Prior Knowledge

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Hong QianAuthors Info & Claims

GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 3 - 4

https://doi.org/10.1145/3638530.3664054

Published: 01 August 2024 Publication History

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Abstract

The Automated Design Competition in GECCO'24 aims to find intelligent 3D agents that perform better in specific environments. To address this problem, this technical report proposes a Cascaded Structure and Parameter Optimization (CaSPO) framework. After constructing an initial population by using prior knowledge, CaSPO optimizes the mechanical structure, control system, and parameters sequentially to find good-performance agents. Experiments in different tasks and environments verify the effectiveness of the proposed CaSPO framework.

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References

[1]

Adam Klejda, Maciej Komosinski, and Agnieszka Mensfelt. 2022. Diversification techniques and distance measures in evolutionary design of 3D structures. In Proceeding of the 2022 Genetic and Evolutionary Computation Conference. Boston, MA, 124--127.

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[2]

Maciej Komosinski and Szymon Ulatowski. 1999. Framsticks: Towards a Simulation of a Nature-Like World, Creatures and Evolution. In Proceedings of the 5th European Conference on Advances in Artificial Life. Lausanne, Switzerland, 261--265.

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[3]

Hong Qian and Yang Yu. 2021. Derivative-free reinforcement learning: A review. Frontiers of Computer Science 15, 6 (2021), 156336.

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Index Terms

Automated Design Competition Technical Report: Cascaded Structure and Parameter Optimization Based on Prior Knowledge
1. Applied computing
  1. Operations research
2. Computing methodologies
  1. Artificial intelligence
  2. Machine learning

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Information

Published In

GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2024

2187 pages

ISBN:9798400704956

DOI:10.1145/3638530

Chair:
Xiaodong Li,
Program Chair:
Julia Handl

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 August 2024

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Funding Sources

National Natural Science Foundation of China
CCF-AFSG Research Fund

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GECCO '24 Companion

Sponsor:

SIGEVO

GECCO '24 Companion: Genetic and Evolutionary Computation Conference Companion

July 14 - 18, 2024

VIC, Melbourne, Australia

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Variations of biogeography-based optimization and Markov analysis

A bilevel optimization approach to automated parameter tuning

Backtracking Search Optimization Algorithm for numerical optimization problems