Alternative Splicing in Evolutionary Computation: Adaptation in Dynamic Environments

Natural organisms have to deal efficiently with changing environments and are thus a great source of inspiration to solve dynamic problems in artificial domains. Dynamic optimisation has gained a lot of interest lately as many real world problems are indeed dynamic. In this paper, we look at post-transcriptional processes and alternative splicing in particular: Although these biochemical processes are gaining increasing attention from the genetics community, they remain relatively unexplored in evolutionary computation. We suggest a simple abstract encoding that allows one to construct multiple expressions from the same template supporting quick adaptation to changes in the cost surface. This encoding enables the system to find, control and reuse groups of building blocks that are being shared by different environments. Tests on a modified version of the dynamic knapsack problem show that it significantly outperforms the canonical genetic algorithm as well as simple implementations of random immigrants and hypermutations.