add paper.txt

paper.txt

@@ -0,0 +1,27 @@
+Abstract
+Neuroevolution is a subfield of artificial intelligence that
+leverages evolutionary algorithms to generate and optimize
+artificial neural networks. This technique has proven to be
+successful in solving a wide range of complex problems
+across various domains. The NeuroEvolution of Augment-
+ing Topologies (NEAT) is one of the most renowned al-
+gorithms in neuroevolution. Characterized by its openness,
+it starts with minimal networks and progressively evolves
+both the topology and the weights of these networks to opti-
+mize performance. However, the acceleration techniques em-
+ployed in prevailing NEAT implementations typically rely on
+parallelism on CPUs, failing to harness the rapidly expand-
+ing computational resources of today. To bridge this gap,
+we present NEATAX, an innovative framework that adapts
+NEAT for execution on hardware accelerators. Built on top
+of the JAX, NEATAX represents networks with varying topo-
+logical structures as tensors with the common shape, facili-
+tating efficient parallel computation using function vectoriza-
+tion. Upon rigorous testing across various tasks, we found
+that NEATAX has the capacity to shrink the computa-
+tion time from hours or even days down to a matter of
+minutes. These results demonstrate the potential of NEATAX
+as a scalable and efficient solution for neuroevolution tasks,
+paving the way for the future application of NEAT in more
+complex and demanding scenarios. NEATAX is available at
+https://github.com/WLS2002/neatax.

pipeline.py

@@ -80,4 +80,8 @@ class Pipeline:
 
         print(f"Generation: {self.state.generation}",
               f"species: {len(species_sizes)}, {species_sizes}",
-              f"fitness: {max_f:.6f}, {min_f:.6f}, {mean_f:.6f}, {std_f:.6f}, Cost time: {cost_time * 1000:.6f}ms")
+              f"fitness: {max_f:.6f}, {min_f:.6f}, {mean_f:.6f}, {std_f:.6f}, Cost time: {cost_time * 1000:.6f}ms")
+
+
+
+