A NEW APPROACH TO CODE GENERATION FOR EFFICIENT NEURAL COMPUTATION IN ACCESSIBLE ARTIFICIAL ECOSYSTEMS

The purpose of this study is to address the critical computational challenges faced in Artificial Life (ALife) research, particularly in achieving scalable simulations of large populations of agents with evolving and heterogeneous neural networks.These simulations are essential for investigating complex phenomena such as adaptation, self-organization, and the emergence of intelligence (noogenesis) in artificial ecosystems.However, the computational overhead associated with evolving neural architectures, especially in open-ended environments with high variability, remains a significant barrier.The methodology proposed in this study employs a dynamic code generation approach that leverages the V8 JavaScript engine for Just-In-Time (JIT) compilation.This approach dynamically transforms neural network topologies into computational graphs, enabling the efficient generation of minimal instruction sets for activation functions, which are then compiled and optimized at runtime.By focusing on active subgraphs and pruning unnecessary computations, this method significantly reduces the overhead typically incurred by interpreting neural networks in each simulation step.The findings demonstrate that the proposed approach outperforms traditional static interpretation methods, as exemplified by the Neataptic library.Benchmarking experiments conducted on an M1 Pro processor showed a 23 % reduction in timestep processing time, with the capability to handle up to thousands of agents efficiently.This scalability ensures the feasibility of running long-term ALife simulations, preserving high biological fidelity while reducing computational costs.The originality of this work lies in its novel application of dynamic code generation to evolving neural networks in ALife models, an area traditionally dominated by frameworks optimized for static neural topologies or batch-based operations.By demonstrating that JavaScript, a non-traditional language for high-performance simulations, can achieve competitive efficiency through runtime optimization, this research broadens the accessibility of ALife simulations to a broader audience, including those focusing on educational or entertainment applications.The practical value of this approach is its potential to enable more extensive and detailed investigations into emergent intelligence and behavior in artificial ecosystems.By overcoming computational limitations, the framework supports longterm studies of noogenesis, offering new insights into how adaptive intelligence and novel behaviors develop in evolving populations.In conclusion, this study presents a scalable, efficient, and flexible solution to the computational challenges of ALife simulations, forming a foundation for future research.Promising directions for further development include distributed simulation architectures and hybrid GPU-accelerated approaches to enhance scalability and performance.