Colony of AI: towards building families of AI-agents using theory of genetic algorithm and bias randomization

This paper proposes a unique concept of colony of artificial intelligence (AI). A colony of AI is defined as a family of AI-agents that mimic the behavior of a biological system. The natural phenomena of biological systems, including colony of ants and colony of humans, is based on the idea that genetic evolution can occur through biological reproduction. Therefore, this paper defines a terminology, “marriage of AI-agents” to allow marriage between two AI-agents to produce unique offspring. It adapts the theory of genetic algorithm and utilizes the crossover and mutation techniques to build a colony of AI. AI models generally consists of a pair of parameters (kernel weight and bias) that they learned from an environment. These parameters are considered AI-genetic information for a colony of AI. A marriage allows the exchange of this AI-genetic information between two AI-agents through a crossover technique to produce a child AI-agent. Mutation is also used to make minor random changes to the child’s AI-genetic information to make it a unique AI-agent. One of the uniqueness of the proposed approach is the bias randomization of a parent AI-agent using Gaussian or uniform distribution (before applying the crossover technique) so that the flexibility to adjust classification boundaries is enhanced. This approach recommends the switching (crossover) of 50% of AI-genetic information from each parent, while modifying the bias parameter of one of the parents. This unique crossover-mutation technique with a modified AI-genetic information allows the parent AI-agents to produce offspring with an increased performance. Simulations are conducted for building a colony of AI using a pretrained VGG16 model and the CIFAR-10 dataset. Current simulations show that the flexibility of a parent (not of both parents) improves the performance of the child AI-agent by 6% (from 72% to 78%) on average with 10 epochs.