Philip McKinley , Betty

N early 150 years ago, Charles Darwin explained how evolution and natural selection transformed the earliest life forms into the rich panoply of life seen today. Scientists estimate this process has been at work on Earth for at least 3.5 billion years. But we remain at the dawn of evolution in another world: the world of computing. There, evolution helps humans solve complex problems in engineering and provides insight into the evolutionary process in nature. As computing power continues to increase, researchers and developers apply evolutionary algorithms to an ever-widening variety of problems. As the " Evolution in a Computer " sidebar shows, evolutionary computation methods such as genetic algorithms have already achieved considerable success, rivaling and surpassing human designers in problem domains as wide-ranging as flash memory sticks and aircraft wings. We are investigating how to harness the power of evolution to help construct better computer software. The increasing interaction between computing technology and the physical world motivates this work. Systems must adapt to their environment, compensate for failures, optimize performance, and protect themselves from attacks—all with minimal human intervention. To design robust and resilient computational systems, we can take inspiration from nature. Living organisms have an amazing ability to adapt to changing environments , both in the short term through phenotypic plasticity and in the longer term through Darwinian evolution. Indeed, no existing cybersystem rivals the complexity of Earth's biosphere, yet life on Earth has evolved to not only deal with this complexity but to thrive on it. Many researchers have studied how to use the characteristics of natural systems to design better computing systems. One approach mimics the behaviors of social insects and other species. However, while such biomi-metic methods have shown promise in controlling fleets of unmanned robotic systems and in other applications, they can only codify behaviors observed in nature today. Purely biomimetic approaches seek to imitate the results of evolution, but they do not account for the process of natural selection that produced those behaviors. For example, we can design the control software on a microrobot so that it mimics certain behaviors found in ants. However, while the robot might possess some physical characteristics reminiscent of an ant, the differences vastly outnumber the similarities. On the other hand, if we had the ability to evolve the control software, taking into account the capabilities of the robot and the characteristics of its environment, new behaviors …