Robustness and Evolvability of Evolutionary Algorithms

进化算法从自然进化中汲取灵感。生成一组不同的候选解决方案，并将其与期望的结果进行比较。 然后，通过多代变异、选择和繁殖，这样的种群适应选择标准，即与期望结果的相对距离，并产生更合适的解决方案。* 自20世纪70年代成立以来，进化算法已经取得了巨大的进步。在过去的几十年里，自然进化的知识在生物学中有了深刻的进步。这一进展在很大程度上还没有被纳入进化的计算模型中，因此不能用于应用。我的研究项目调查生物进化的新发展，并将其纳入设计更智能的算法，以解决更雄心勃勃的应用问题。* 我对生物进化的一些基本问题特别感兴趣：为什么生物体是可进化的？为什么随机变异并不总是有害的，它又如何成为适应性进化的驱动力呢？自然进化系统对遗传和环境扰动表现出巨大的适应力，以及产生适应性新表型的巨大能力。鲁棒性和进化性是生命系统的基本属性，研究它们之间的关系是理解进化核心机制的关键。这些一直是生物学中许多理论和实证研究的焦点。然而，它们在进化计算中还没有得到足够的关注，并且在推进我们的算法设计方面具有巨大的潜力。我建议定量描述进化算法的鲁棒性和可进化性。稳健性是冗余基因型-表型映射的结果，其中不同的基因型可以编码相同的表型。也就是说，突变可以产生中性和可观察的表型结果。研究鲁棒性和可进化性的关系可以启发设计一种新的算法，该算法具有更好的表示方案，能够对随机变化具有高耐受性，并具有高适应性以探索新的表型。这将极大地提高算法的搜索效率，推动进化计算领域的发展.然后，新算法将应用于复杂的生物医学知识挖掘问题。同时，这项研究的发现有助于阐明生物系统进化的核心机制。进化算法提供了研究自然进化的可能性，与传统生物学非常不同。在计算进化中，在一个实际的时间轴中允许多达数百万代的进化，并且进化过程是完全可控的、易处理的和可重复的。这些特征远远超出了生物学的实验研究。

Evolutionary algorithms draw inspiration from natural evolution. A population of diverse candidate solutions is generated and compared to a desired outcome. Then, through multiple generations of variation, selection, and reproduction, such a population adapts to the selection criteria, i.e. relative distance from the desired outcome, and produces fitter solutions. ***Evolutionary algorithms have seen enormous progress since they were founded in the 1970s. The knowledge of natural evolution has improved profoundly in biology in the past decades. This progress has, to a large degree, not yet been incorporated into computational models of evolution and, therefore, cannot be harvested for applications.***My research program investigates new developments in biological evolution and incorporates them into designing more intelligent algorithms for more ambitious application problems. ***Some fundamental questions on biological evolution seem particularly interesting to me. Why are living organisms evolvable? Why isn't random variation always harmful, and how can it be the driving force for adaptive evolution? Natural evolutionary systems show tremendous resilience to genetic and environmental perturbations, as well as great capabilities in generating adaptive new phenotypes. Robustness and evolvability are fundamental properties of living systems, and investigating their relationship is key to understanding core mechanisms of evolution. These have been the focus of numerous theoretical and empirical studies in biology. However, they have not yet received adequate attention in evolutionary computing, and hold great potential in advancing our algorithm design.***I propose to quantitatively characterize robustness and evolvability in evolutionary algorithms. Robustness is a result of the redundant genotype-to-phenotype mapping, where different genotypes can encode the same phenotype. That is, mutations can yield both a neutral and an observable phenotypic outcome. Investigating the relationship of robustness and evolvability can inspire the design of a new algorithm with a better representation scheme that enables both high tolerance to random variations and high adaptivity to explore novel phenotypes. This can improve the search efficiency of algorithms and advance the field of evolutionary computing profoundly. The new algorithm will then be applied to complex biomedical knowledge mining problems.***Meanwhile, the findings of this research can help elucidate core mechanisms of evolution in biological systems. Evolutionary algorithms provide the possibility of studying natural evolution very differently from traditional biology. In computational evolution, up to millions of generations are allowed in a practical timeline, and the evolution process is fully controllable, tractable, and repeatable. These features go far beyond experimental studies in biology.