生命基本功能的遗传蓝图编码在 DNA 中，DNA 被翻译成蛋白质——驱动我们大多数代谢过程的引擎。近期基因组测序的进展揭示了极其多样的蛋白质家族，但与所有可能氨基酸序列构成的庞大搜索空间相比，已知功能性家族的集合仍然极小。可以说，自然界拥有的蛋白质“词汇量”是有限的。因此，计算生物学家面临的主要问题是：这一词汇量能否扩展，以纳入很久以前灭绝、或者可能从未进化出来的有用蛋白质。我们概述了解决该问题的一种计算方法。通过融合进化算法、机器学习（ML）和生物信息学，我们可以促进开发前所未有的全新蛋白质。我们设想这项工作将形成计算进化的一个新子领域，并将其命名为模拟分子进化的进化算法（EASME）。
英文摘要：
The genetic blueprint for the essential functions of life is encoded in DNA, which is translated into proteins -- the engines driving most of our metabolic processes. Recent advancements in genome sequencing have unveiled a vast diversity of protein families, but compared to the massive search space of all possible amino acid sequences, the set of known functional families is minimal. One could say nature has a limited protein "vocabulary." The major question for computational biologists, therefore, is whether this vocabulary can be expanded to include useful proteins that went extinct long ago, or maybe never evolved in the first place. We outline a computational approach to solving this problem. By merging evolutionary algorithms, machine learning (ML), and bioinformatics, we can facilitate the development of completely novel proteins which have never existed before. We envision this work forming a new sub-field of computational evolution we dub evolutionary algorithms simulating molecular evolution (EASME).
Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness
University of Granada（格拉纳达大学）；Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI)（安达卢西亚数据科学与计算智能研究所（DaSCI））；TECNALIA（泰克纳利亚）；Basque Research & Technology Alliance (BRTA)（巴斯克研究与技术联盟（BRTA））；University of the Basque Country (UPV/EHU)（巴斯克大学（UPV/EHU））
Linear Proposal Operators and Stochastic Search Geometry in SOMA and Differential Evolution
VSB - Technical University of Ostrava（俄斯特拉发技术大学（VSB））；IT4Innovations National Supercomputing Center（IT4Innovations国家超级计算中心）；Marine Research Institute, Klaipeda University（克莱佩达大学海洋研究所）
Dynamic Detection of Relevant Objectives and Adaptation to Preference Drifts in Interactive Evolutionary Multi-Objective Optimization
Centre for Logistics & Sustainability Analytics, Kent Business School, University of Kent（肯特大学肯特商学院物流与可持续性分析中心）；Université de Haute-Alsace（上阿尔萨斯大学）；Alliance Manchester Business School, University of Manchester（曼彻斯特大学联盟曼彻斯特商学院）
