Grant: Discovery Grants Program - Individual

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 isnt 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.