Code Farming: A Process for Creating Generic Computational Building Blocks
This work addresses a specific bottleneck in genetic programming for researchers and practitioners, offering an incremental improvement over existing seeding techniques.
The paper tackles the problem of inefficient initial population seeding in genetic programming by introducing a process to automatically create generic computational building blocks, which reduces the need for random rediscovery and human intervention compared to standard methods.
Motivated by a desire to improve on the current state of the art in genetic programming, and aided by recent progress in understanding the computational aspects of evolutionary systems, we describe a process that creates a set of generic computational building blocks for the purpose of seeding initial populations of programs in any genetic programming system. This provides an advantage over the standard approach of initializing the population purely randomly in that it avoids the need to constantly rediscover such building blocks. It is also better than seeding the initial population with hand-coded building blocks, since it lessens the amount of human intervention required by the system.