
Rohan P. 2025 | BASIS Independent Fremont
- Project Title: Analytical and Computational Analysis of the Boundedness of N-compounded Systems, a Choas Theory Study OR Developing a Digital Student Access Control and Notification System
- BASIS Independent Advisor: Mr. Vidal
This project explores the simulation of natural selection within artificial ecosystems using Artificial Intelligence (AI) agents equipped with neural network-based intelligence. By integrating evolutionary AI algorithms (EA) with neural networks, the study investigates How environmental factors and agent-specific attributes influence evolutionary outcomes and emergent behaviors. AI agents, designed with variable traits (agent-specific attributes) such as size, speed, intelligence, and eyesight, Are placed in a dynamic environment where survival and reproduction depend on acquiring energy through food consumption. Evolutionary principles Are applied across generations, such as mutations introducing variability in neural network configurations, repopulation of agents under the successful criterion (acquiring food), and elimination of agents not under the successful criterion.
The research utilizes Python to develop the simulation, combining generational AI algorithms with an evolved neural topology framework to drive the decisions of the agent. Environmental variables (environmental factors) such as food abundance, proportions of rotten food, and spatial dimensions Are manipulated to observe their effects on population dynamics and behavioral evolution. Initial investigations focus on basic survival behaviors, while subsequent experiments introduce more complex phenomena, including aggression, competition, and altruism.
The study provides insights into the dynamics of natural selection and the emergence of complex behaviors from simple computational rules. The application of this simulation demonstrates a model for biological processes like the adaptation of bacteria, single-celled organisms with limited function and simplistic behavior. Future extensions could explore advanced strategies for simulating cooperative behaviors and multi-agent interactions. This work contributes to artificial life, evolutionary computation, and the design of adaptable AI systems, bridging computational simulations with insights into biological and psychological behaviors.