HybrID: A hybridization of indirect and direct encodings for evolutionary computation

Author(s): 
Clune J
Beckmann BE
Pennock RT
Ofria C
Year: 
2009
Abstract: 

Evolutionary algorithms typically use direct encodings, where each element of the phenotype is specified independently in the genotype. Because direct encodings have difficulty evolving modular and symmetric phenotypes, some researchers use indirect encodings, wherein one genomic element can influence multiple parts of a phenotype. We have previously
shown that HyperNEAT, an indirect encoding, outperforms FT-NEAT, a direct-encoding control, on many problems, especially as the regularity of the problem increases. However, HyperNEAT is no panacea; it had difficulty accounting for irregularities in problems. In this paper, we propose a new algorithm, a Hybridized Indirect and Direct encoding (HybrID), which discovers the regularity of a problem with an indirect encoding and accounts for irregularities via a direct encoding. In three different problem domains, HybrID outperforms HyperNEAT in most situations, with performance improvements as large as 40%. Our work suggests that hybridizing indirect and direct encodings can be an effective way to improve the performance of evolutionary algorithms.


Evolving artificial neural networks with generative encodings inspired by developmental biology

In this dissertation I (Jeff Clune) investigate the difference between generative encodings and direct encodings for evolutionary algorithms. Generative encodings are inspired by developmental biology and were designed, in part, to increase the regularity of synthetically evolved phenotypes. Regularity is an important design principle in both natural organisms and engineered designs. The majority of this dissertation focuses on how the property of regularity enables a generative encoding to outperform direct encoding controls, and whether a bias towards regularity also hurts the performance of the generative encoding on some problems. I also report on whether researchers can bias the types of regularities produced by a generative encoding to accommodate user preferences. Finally, I study the degree to which a generative encoding produces another important design principle, modularity.

Several previous studies have shown that generative encodings outperform direct encodings on highly regular problems. However, prior to this dissertation, it was not known how generative encodings compare to direct encodings on problems with different levels of regularity. On three different problems, I show that a generative encoding can exploit intermediate amounts of problem regularity, which enabled the generative encoding to increasingly outperform direct encoding controls as problem regularity increased. This performance gap emerged because the generative encoding produced regular artificial neural networks (ANNs) that produced regular behaviors. The ANNs evolved with the generative encoding contained a diverse array of complicated, regular neural wiring patterns, whereas the ANNs produced by a direct encoding control were irregular.

I also document that the bias towards regularity can hurt a generative encoding on problems that have some amount of irregularity. I propose a new algorithm, called HybrID, wherein a generative encoding produces regular patterns and a direct encoding modifies
those patterns to provide fitness-enhancing irregularities. HybrID outperformed a generative encoding alone on three problems for nearly all levels of regularity, which raises the question of whether generative encodings may ultimately excel not as stand-alone algorithms, but by being hybridized with a further process of irregular refinement.

The results described so far document that a generative encoding can produce regular solutions. I then show that, at least for the generative encoding in this case study, it is possible to influence the types of regularities produced, which allows domain knowledge and preferences to be injected into the algorithm. I also investigated whether the generative encoding can produce modular solutions. I present the first documented case of this generative encoding producing a modular phenotype on a simple problem. However, the generative encoding's inability to create modularity on harder problems where modularity would have been beneficial suggests that more work is needed to increase the likelihood that this encoding produces modular ANNs in response to challenging, decomposable problems.

Overall, this dissertation paints a more complete picture of generative encodings than prior studies. Initially, it demonstrates that, by producing regular ANNs and behaviors, generative encodings increasingly outcompete direct encodings as problem regularity increases. It next documents that a bias towards regularity can harm the performance of direct encodings when problems contain irregularities. The HybrID algorithm suggests a path forward, however, by revealing that a refinement process that fine-tunes the regular patterns produced by a generative encoding can boost performance by accounting for problem irregularities. Finally, the dissertation shows that the generative encoding studied can produce modular networks on simple problems, but may struggle to do so on harder problems. The general conclusion that can be drawn from this work is that generative encodings can produce some of the properties seen in complex, natural organisms, and will likely be an important part of our long-term goal of synthetically evolving phenotypes that approach the capability, intelligence, and complexity of their natural rivals.

Evolving artificial neural networks with generative encodings inspired by developmental biology

In this dissertation I (Jeff Clune) investigate the difference between generative encodings and direct encodings for evolutionary algorithms. Generative encodings are inspired by developmental biology and were designed, in part, to increase the regularity of synthetically evolved phenotypes.

Pub. Info: 
Proceedings of the European Conference on Artificial Life (ECAL). Vol. 2: 134: 141
BibTeX: 

@inproceedings{Clune:2009:HHI:2017762.2017782,
author = {Clune, Jeff and Beckmann, Benjamin E. and Pennock, Robert T. and Ofria, Charles},
title = {HybrID: A Hybridization of Indirect and Direct Encodings for Evolutionary Computation},
booktitle = {Proceedings of the 10th European Conference on Advances in Artificial Life: Darwin Meets Von Neumann - Volume Part II},
series = {ECAL'09},
year = {2011},
isbn = {978-3-642-21313-7},
location = {Budapest, Hungary},
pages = {134--141},
numpages = {8},
url = {http://dl.acm.org/citation.cfm?id=2017762.2017782},
acmid = {2017782},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
keywords = {artificial neural networks, evolutionary algorithms, indirect (generative, developmental) encodings (representations), neuroevolution},
}