IES Organizing Committee，日本機械学会中国四国支部
Artificial Evolution meets Metagenomics: the Binomic GA for Evolving Symbiotic Systems
Dr. Inman Harvey， The University of Sussex， UK
Evolutionary Robotics (ER) has now been used for many years for exploring the space of possible artificial brains， or brains + bodies for robots and other AI or Artificial Life systems. For interesting behaviours these will be complex non-linear feedback systems， which are inherently difficult to design by conventional methods. Clearly natural Darwinian evolution has been successful for biological systems， hence it is thought to be useful for ER to copy this with Artificial Evolution.
The conventional picture is a Genetic Algorithm (GA) with a population (of genetically specified artificial systems， initially random)， and successive generations where the fitter members of each generation act as parents to pass on some of their characteristics， via their genes， to their offspring; over extended time， the population should evolve to become fitter. Fitness is here defined by the aims of the human controlling the artificial evolution， much as a farmer applies artificial selection to the breeding of cattle or rice. This conventional picture has vertical genetic transfer， from one generation to the next and fitness is calculated for each individual. However very recent Metagenomic studies have painted a rather different picture of the microbial evolution that has comprised the first 2 billion years of evolution on this planet， together with the majority of evolution even today. For instance， a human being is a walking ecosystem that has coevolved with the microbial cells within; only 1% of the cells within the typical human-microbial ecosystem are actually human， and they are all coexisting in symbiosis.
So this new picture of evolution is one of horizontal gene transfer — genes are swapped between microbes without the need for successive generations; and evaluation of fitness is at the symbiotic or community level， based on the viability of an ecosystem. Though one or other of these aspects have featured in some evolutionary computation before， here in the Binomic GA we incorporate both together for the first time. With rather simple code， we can evolve populations of component parts of a complex system， where fitness is evaluated at the system-level， and gene-transfer acts horizontally between different component parts.
This methodology is appropriate for complex systems where the behaviour of interest is at the community level arising from interactions between component parts: for instance individual agents within an agent swarm， or artificial neurons and connections within an Artificial Neural Network (ANN)， or antibodies within an artificial immune system. We demonstrate its effectiveness with an ANN example.