When I started working on the mathematical description of evolution called genetic algorithms, over 40 years ago, I hardly imagined the range of problems that genetic algorithms could solve.2 Today, scientists and engineers from fields as diverse as agriculture, synthetic biology, and mechanical engineering are using genetic algorithms to find efficient solutions to their problems.
I'm no engineer, so I've applied genetic algorithms to the area I know and like best: complex adaptive systems. These are systems that have multiple levels of co-evolution, like ecosystems, which through their complex interactions, become more than the sum of their parts. One example is the immune system, a conglomerate of cells, cytokines and proteins, containing many levels of interaction and adaptation which provide the power to protect a human from the majority of pathogens encountered. To be able to capture the essence of these complex adaptive systems is to be ...
