Physical Evolutionary Computation

Although evolutionary computation (EC) is an “embarrassingly parallel” process, it is often deployed on essentially serial machines, and even its parallel implementations typically retain the globally synchronized and regimented style typical of serial computation. We explore a radical ‘physical evolutionary computation’ (PEC) hardware/software framework that is based on real time and real space rather than an abstract sequence of events—for example, the mutation rate is specified in Hertz. PEC supports massive and reconfigurable parallelism, using a prototype hardware ‘tile’ that begins evolutionary computation within three seconds of applying power. Although each tile is a simple computer by today’s standards, tiles can be plugged together into a wide variety of size and shape computing grids—even while the computation is running. We present our initial explorations with this framework, defining mechanisms for representing and sharing problems, and mapping between computational spaces and physical ones. We discuss advantages of PEC—such as extremely robust operation—as well as its challenges, and touch on some unexpected, and potentially useful, level-crossing interactions that can be explored when the embedding of the computational within the physical is made real. 1 Scalable abstractions Abstract computational models simplify the natural world to help us understand it, and also serve as outlines for the engineering of—preferably ever-morepowerful—computing machines. Researchers have explored computing models inspired by everything from mathematics and philosophy to the natural and social sciences, but abstractions based algorithmic software on reliable serial hardware have dominated computer science and engineering, accruing volumes of widely-understood tools and techniques. The basic von Neumann machine ‘CPU+memory’ model abstracts physics into deterministic boolean algebra, and abstracts time into a total ordering of discrete steps from a beginning to an end. Even more aggressively, it eliminates the distances and dimensions of physical space entirely, declaring all memory equally cheap to access by the CPU. Where that assumption is invalid, the von Neumann machine is simply a poor descriptive model. On the other hand, when used for machine design, that assumption has held simply because engineering was commanded to make it so, which it has done spectacularly for decades, scaling clock speed and memory capacity over many orders of magnitude.computational models simplify the natural world to help us understand it, and also serve as outlines for the engineering of—preferably ever-morepowerful—computing machines. Researchers have explored computing models inspired by everything from mathematics and philosophy to the natural and social sciences, but abstractions based algorithmic software on reliable serial hardware have dominated computer science and engineering, accruing volumes of widely-understood tools and techniques. The basic von Neumann machine ‘CPU+memory’ model abstracts physics into deterministic boolean algebra, and abstracts time into a total ordering of discrete steps from a beginning to an end. Even more aggressively, it eliminates the distances and dimensions of physical space entirely, declaring all memory equally cheap to access by the CPU. Where that assumption is invalid, the von Neumann machine is simply a poor descriptive model. On the other hand, when used for machine design, that assumption has held simply because engineering was commanded to make it so, which it has done spectacularly for decades, scaling clock speed and memory capacity over many orders of magnitude. Of course, many different abstractions can be imposed on any situation, and the continued dominance of serial algorithmic computing is now uncertain as its scalability dwindles and it yields ground to alternate models such as cache-coherent multicores [Sutter, 2005], which offer a degree of parallelism while preserving many of the computational and spatiotemporal abstractions of the classic framework. This paper explores the physical evolutionary computation (PEC) framework (Figure 1), which ab-