Monthly Archives: February 2015

Distributed Interactive Genetic Algorithms / Distributed Evolutionary Computation

Distributed Interactive Genetic Algorithms are similar to Interactive Genetic Algorithms with additional features such as lack of a single point of failure, redundancy and potentially anonymous scalable human participation in the process.

Traditionally the Interactive Genetic Algorithm relies on the computer to be in the “innovator” role. The human being typically is in the “selector” role. Often the Evolutionary Computation  is done by a single human sitting in front of a computer who must evaluate the quality of the candidate solutions proposed by the innovator which in this case would be the computer.

An example of a typical Interactive Genetic Algorithm would be the human/computer interaction which generates through Evolutionary Computation the camouflage patterns on military outfits. Additional examples would be to use the Evolutionary Computation process to generate efficient designs for unmanned drones or computer chips.

One of the potential bottlenecks in using Interactive Genetic Algorithms is due to the human who may suffer from fatigue because the human has to ultimately judge the aesthetic quality of each candidate solution.

Distributed Interactive Genetic Algorithms / Distributed Evolutionary Computation would be similar to taking both the humans, the computer processing and putting them both in the cloud. In this descriptive example we could say that Ethereum has the ability to handle the distributed computation necessary and at the same time the humans could be given monetary incentive to participate in the selection process. In that example you would have the makings of a Distributed Interactive Genetic Algorithm running on the Ethereum platform. An example use case could be a distributed content based media retrieval system as has been already accomplished centralized using an Interactive Genetic Algorithm (Patel, Meshram, 2012).


Reynolds, C. (2010, July). Using interactive evolution to discover camouflage patterns. In ACM SIGGRAPH 2010 Posters (p. 113). ACM.
Patel, B. V., & Meshram, B. B. (2012). Content based video retrieval systems. arXiv preprint arXiv:1205.1641.

Distributed Human-based Genetic Algorithms

What we know is that human based genetic algorithms can utilize the Darwinian process of “natural selection” to evolve a candidate solution. Human based genetic algorithms utilize the human as the selector and innovator. The data itself represents the sequence.

The sequence is a data pattern. The human beings contribute data patterns such as the”unique content” which makes the human beings the innovators. The human beings also are the selectors because they determine which content is “fit” or “unfit”.  This can be managed by “like and dislike” or “upvote and downvote”.

An example of this would be an Ask site where human beings can ask questions and where answers selected up.  Additional examples would include sites like Digg, Reddit, and Stumbleupon. The problem with these kinds of human based genetic algorithms is that there are centralized entities which means the website can be shut down. Distributed Human-based Genetic Algorithms would be not have a single point of failure and can be run on decentralized autonomous platforms such as Ethereum, NXT and any similar platform which allows for scriptability.


Okano, J., Hamano, K., Ohnishi, K., & Koppen, M. (2014, October). Particular fine-grained parallel GA for simulation study of distributed human-based GA. InSystems, Man and Cybernetics (SMC), 2014 IEEE International Conference on(pp. 3508-3513). IEEE.