Catalytic Search in Dynamic Environments

Catalytic Search is an optimization algorithm inspired by random catalytic reaction networks and their pre-evolutionary dynamics. It runs within an Artificial Chemistry in which reactions can be reversible, and replication is not taken for granted. In previous work one of us had shown that although inherently slower than Evolutionary Algorithms, Catalytic Search is able to solve simple problems while naturally maintaining diversity in the population. This is a useful property when the environment may change. In this paper, we compare the performance of Catalytic Search and a Genetic Algorithm in a dynamic environment represented by a periodically changing objective function. We investigate the impact of parameters such as temperature, inflow/outflow rate, and amount of enzymes. We show that Catalytic Search is generally more stable in the face of changes, although still slower in achieving the absolute best fitness. Our results also offer some indications on how catalytic search could either degenerate into random search, or progress towards evolutionary search, although the latter transition has not been fully demonstrated yet.