This is a growing paper list for parallel and distributed evolutionary computation (PDEC). Currently we are actively updating it (at least from 2021 to 2023). Owing to the abundance of the related literature, however, we believe that much interesting work is still missed here. If you find them missed, welcome to contact with us via Issues or Pull requests to add.
A (Relatively Big) Family of Evolutionary Algorithms (EAs)
Here, we consider a relatively big family of evolutionary algorithms (and also several closely related techniques, e.g., random search and simulated annealing), as presented below. Since here we focus primarily on their parallel/distributed versions and variants, we also provide a reference list for their original / seminal / landmark / survey / review papers, in order to help better understand them (especially for newcomers). We strongly suggest to see e.g. 2015's Review paper in Nature or 1993's Review paper in Science for more details.
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Four Conventional EAs
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- Covariance Matrix Adaptation ES (CMA-ES)
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Two Swarm Intelligence (SI) Siblings
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Two Representative Multi-Objective Optimization (MOO) Evolutionary Frameworks
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Several Relatively New Extensions/Improvements/Variants
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Co-Evolutionary Algorithms (CEA)
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Differential Evolution (DE)
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Memetic Algorithms (MA)
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- Multidimensional Archive of Phenotypic Elites (MAP-Elites)
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- Open-Ended Evolution
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Common Individual-based Counterparts/Baselines/Competitors (especially for their stochastic versions)
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Random Search (RS)
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Local Search (LS)
- Hill Climbers (HC)
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Simulated Annealing (SA)
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This ongoing paper list for PDEC is now supported by Shenzhen Fundamental Research Program under Grant No. JCYJ20200109141235597 (￥2,000,000), granted to Prof. Yuhui Shi (CSE, SUSTech @ Shenzhen, China), and actively maintained/updated (from 2021 to 2023) by his group members (e.g., Qiqi Duan, Chang Shao, Guocheng Zhou, Mingyang Feng, Minghan Zhang, Youkui Zhang, and Qi Zhao).
We also acknowledge the additional contributions from Vincent A. Cicirello. We welcome the recent (from 2022) contributions from Jian Zeng (focusing on data mining).