This research seeks to develop evolutionary methods for constructing deep neural networks, offering potential improvements to machine learning techniques by modeling adaptive architectures under selective pressures. Purpose. The goal of the work is to explore the dynamics of neural complexity in artificial life agents exposed to progressively challenging environments. Research methods. We conducted a two-dimensional simulation to model populations of agents with evolving neural networks and physical forms. The environment progresses from simple conditions to increasingly complex scenarios, including static walls, moving obstacles, hazardous zones, and lethal poisons. Our approach builds on fundamental artificial life systems such as Tierra, Avida, and PolyWorld. The neural architectures evolve based on principles inspired by the NeuroEvolution of Augmenting Topologies. We apply the Tononi–Sporns–Edelman complexity measure to evaluate neural integration and specialization, helping us understand how agents adapt their networks to achieve a balance between global coherence and localized functionality. Results. Research indicated that while complex environments can temporarily enhance neural sophistication, harsher conditions often favor simpler, more prolific reproductive r-strategies. Effect, populations may create reflex-driven, stimulus-response behaviors instead of developing complex neural structures. Conclusions. These findings enhance our understanding of adaptive intelligence and guide approaches for designing scalable, matching learning systems in robotics and deep neural network architecture development, contributing to the broader goal of understanding how artificial intelligence should evolve. We propose utilizing a recursive genetic algorithm to optimize these balance challenges, promoting long-term neural adaptation to dynamic environments.
A Mon, study studied this question.