Challenges for biologically-inspired computing

• Authors: Russ Abbott
• Venue: GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
• Year: 2005

We discuss a number of fundamental areas in which biologicallyinspired computing has so far failed to mirror biological reality.These failures make it difficult for those who study biology (andmany other scientific fields) to benefit from biologically inspiredcomputing.1. The apparent impossibility of finding a base level at whichto model biological (or most other real-world) phenomena. Althoughmost computer systems are stratified into disjoint and encapsulatedlevels of abstraction (sometimes known as layered hierarchies), theuniverse is not.2. Our inability to characterize on an architectural level theprocesses that define biological entities in both enough detail andwith sufficient abstraction to model them.3. Our inability to model fitness except in terms ofartificially defined functions or artificially defined fitnessunits. Fitness to an environment is not (a) a measure of anentity's conformance to an ideal, (b) an entity's accumulation ofwhat might be called "fitness points," or even (c) a measure ofreproductive success. Fitness to an environment is an entity'sability to acquire and use the resources available in thatenvironment to sustain and perpetuate its life processes.4. Our inability to build models that allow emergent phenomenato add themselves (and their relationships to other phenomena) backinto our models as first class citizens.These failures arise out of our inability as yet to fullyunderstand what we mean by emergence.As an initial step towards surmounting these hurdles, we attemptto clarify what the problems are and to offer a framework in termsof which we believe they may be understood. We also offer adefinition of emergence as the appearance of a persistent processthat produces a area of relatively reduced entropy.