Achieving High-Level Functionality through Evolutionary Complexification (2003)
An appropriate but challenging goal for evolutionary computation (EC) is to evolve systems of biological complexity. However, specifying complex structures requires many genes, and searching for a solution in such a high-dimensional space can be intractable. In this paper, we propose a method for finding high-dimensional solutions incrementally, by starting with an initial population of very small genomes and gradually complexifying those genomes by adding new genes over generations. That way, search begins in an easily-optimized low-dimensional space and increments into increasingly high-dimensional spaces. We describe an existing method for implementing complexification, and further propose that combining complexification with an indirect genetic encoding, in which genes are reused in the specification of the phenotype, can lead to the discovery of highly complex solutions.
In Proceedings of the AAAI-2003 Spring Symposium on Computational Synthesis, Stanford, CA, 2003. AAAI Press.

Risto Miikkulainen Faculty risto [at] cs utexas edu
Kenneth Stanley Postdoctoral Alumni kstanley [at] cs ucf edu
NEAT Matlab The Matlab NEAT package contains Matlab source code for the NeuroEvolution of Augmenting Topologies method (see the orig... 2003