Notes on the simulation of evolution.

The simulation of evolution for the purposes of parameter optimization has generally demonstrated itself to be a robust and rapid optimization technique. But there may exist more value in simulating evolution than simple parameter optimization. The optimizations of system behavior obtained through simulated evolution represent a potentially powerful autopoetic pathway to machine learning and self-organization. Indeed, it may eventually prove to be the only practical path to the development of ontogenetic machine intelligence. As the complexity of the systems being evolved increases, the development of a proper philosophy of analysis and design becomes imperative. The designer of evolutionary algorithms must keep clearly in mind what is being evolved and what evolves only by consequence. Notes on the simulation of evolution are offered in four sections: 1) the basic nature of evolution, 2) its practical simulation, 3) common philosophical errors, and 4) that which remains to be accomplished. Simulated evolutionary optimization is a mechanism of machine learning that can reasonably be expected to continue to grow in importance and practical benefit. As the availability of massively parallel processors increases, the value of simulated evolutionary techniques will become increasingly apparent, if for no other reason than the natural match between the technique and the emerging technology. The method has been repeatedly demonstrated to successfully find points of global optimality when other methods fail, often astonishingly quickly. Specific rules have become apparent and are easily stated.