Evolutionary methods for problem solving and artificial development

Distributed Human-based Genetic Algorithms

superhuman1What 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.


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Distributed Interactive Genetic Algorithms / Distributed Evolutionary Computation


Image Credit, Keith B. WIley (source http://arxiv.org/abs/1111.6131)

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).

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Evolutionary Computation as a Form of Organization

The Free Knowledge Exchange (FKE) project intro-
duces the concept of evolutionary knowledge manage-
ment based on concepts of GA. It used a human-based
genetic algorithm (HBGA) for the task of collabora-
tive solving of problems expressed in natural language
(Kosoruko , 2000a). It was created in 1997 for a small
organization with the goal of promoting success of
each member through new forms of cooperation based
on better knowledge management.

Human genetic based algorithms pave the way for evolutionary self organizing architectures. These architectures can be social, political, economic, or physical.


The idea is that user preferences are tracked in real time by the architecture itself. The architecture then uses this feedback to continuously evolve the organization.

The idea of human interaction came from interac-
tive genetic algorithms (IGA) that introduced hu-
man evaluation interfaces in evolutionary computa-
tion. Human-based genetic algorithm (HBGA) used
in FKE is basically an IGA combined with human-
based innovation interfaces (crossover and mutation).

The concept of Evolutionary Computation as a Form of Organization will be discussed in future postings within the context of how a distributed autonomous virtual state can utilize evolutionary computation to become a self optimizing system.



Evolutionary methods for problem solving and artificial development

One of the principles I follow for problem solving is that many of the best solutions can be found in nature. The basic axiom of all knowledge as self knowledge applies to the study of computer science and artificial intelligence.

By studying nature we are studying ourselves and what we learn from nature can give us initial designs for DApps (decentralized applications).

The SAFE Network example

SAFE Network for example is following these principles by utilizing biomimicry (ant colony algorithm) for the initial design of the SAFE Network. If SAFE Network is designed appropriately then it will have an evolutionary method so that over time by our participation with it can fine tune it. There should be both a symbiosis between human and AI as well as a way to make sure changes are always made according to the preferences of mankind. In essence SAFE Network should be able to optimize it’s design going into the future to meet human defined “fitness” criteria. How they will go about achieving this is unknown at this time but my opinion is that it will require a democratization or collaborative filtering layer. A possible result of SAFE Network’s evolutionary process could be a sort of artificial neuro-network.

The Wikipedia example

Wikipedia is an example of an evolving knowledge resource. It uses an evolutionary method (human based genetic algorithm) to curate, structure and maintain human knowledge. Human beings

One of the main problems with WIkipedia is that it is centralized and that it does not generate any profits. This may be partially due to the fact that the ideal situation is that knowledge should be free to access but it does not factor in that knowledge isn’t free to generate. It also doesn’t factor in that knowledge has to be stored somewhere and that if Wikipedia is centralized then it can be taken down just as the library of Alexandria once was. A decentralized Wikipedia could begin it’s life by mirroring Wikipedia and then use the evolutionary methods to create a Wikipedia which does not contain the same risk profile or model.

Benefits of applying the evolutionary methods to Wikipedia style DApps

super-pedigreeOne of the benefits is that is that there could be many different DApps which can compete in a market place so that successful design features could result in an incentive to continue to innovate. We can think of the market in this instance as the human based genetic algorithm where all DApps are candidate solutions to solve the problem of optimizing knowledge diffusion. The human beings would be the innovators, the selectors, and the initializers. The token system would represent the incentive layer but also be for signalling so that humans can give an information signal which indicates their preferences to the market.

Wikipedia is not based on nature currently and does not evolve it’s design to adapt to it’s environment. Wikipedia “eats” when humans donate money to a centralized foundation which directs the development of Wikipedia. A decentralized evolutionary model would not have a centralized foundation and Wikipedia would instead adapt it’s survival strategy to it’s environment. This would mean Wikipedia following the evolutionary model would seek to profit in competition with other Wikipedia’s until the best (most fit) adaptation to the environment is evolved. Users would be able to use micropayments to signal through their participation and usage which Wikipedia pages are preferred over others and at the same time you can have pseudo-anonymous academic experts with good reputations rate the accuracy.

In order for the human based genetic algorithm to work, in order for the collaborative filtering to work, the participants should not know the scores of different pages in real time because this could bias the results. Also participants do not need to know what different experts scored different pages because personality cults could skew the results and influence the rating behavior of other experts. Finally it would have to be global and decentralized so that experts cannot easily coordinate and conspire. These problems would not be easy to solve but Wikipedia currently has similar problems in centralized form.

Artificial development as a design process

Quote from artificial development:
Human designs are often limited by their ability to scale, and adapt to chang-ing needs. Our rigid design processes often constrain the design to solving the
immediate problem, with only limited scope for change. Organisms, on the other hand, appear to be able to maintain functionality through all stages of de-
velopment, despite a vast change in the number of cells from the embryo to a mature individual. It would be advantageous to empower human designs with
this on-line adaptability through scaling, whereby a system can change com-plexity depending on conditions.
The quote above summarizes one of the main differences between an evolutionary design model and a human design model. The human designs have limited adaptability to the environment because human beings are not good at trying to predict and account for the possible disruptive environmental changes which can take place in the future. Businesses which take on these static inflexible human designs are easily disrupted by technological changes because human beings have great difficulty making a design which is “future proof”.  It is my own conclusion that Wikipedia in it’s current design iteration suffers from this even though it does have a limited evolutionary design. The limitation of Wikipedia is that the foundation is centralized and it’s built on top of a network which isn’t as resilient to political change as it could be. In order for the designs of DApps to be future proof they have to utilize evolutionary design models. Additionally it would be good if DApps are forced to compete against each other for fitness so that the best evolutionary design models rise to the top of the heap.



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.
Reynolds, C. (2010, July). Using interactive evolution to discover camouflage patterns. InACM SIGGRAPH 2010 Posters (p. 113). ACM.
Patel, B. V., & Meshram, B. B. (2012). Content based video retrieval systems. arXiv preprint arXiv:1205.1641.
Coello, C. A. C. (2010). List of references on constraint-handling techniques used with evolutionary algorithms. Power, 80(10), 1286-1292.
Kosorukoff, A., & Goldberg, D. E. (2002, July). Evolutionary Computation As A Form Of Organization. In GECCO (Vol. 2002, pp. 965-972).
Clune, J., Beckmann, B. E., Pennock, R. T., & Ofria, C. (2011). HybrID: A hybridization of indirect and direct encodings for evolutionary computation. In Advances in Artificial Life. Darwin Meets von Neumann (pp. 134-141). Springer Berlin Heidelberg.
Cussat-Blanc, S., Bredeche, N., Luga, H., Duthen, Y., & Schoenauer, M. Artificial Gene Regulatory Networks and Spatial Computation: A Case Study.
Doursat, R. (2008). Organically grown architectures: Creating decentralized, autonomous systems by embryomorphic engineering. In Organic computing (pp. 167-199). Springer Berlin Heidelberg.
Harding, S., & Banzhaf, W. (2008). Artificial development. In Oganic Computing (pp. 201-219). Springer Berlin Heidelberg.
Ulieru, M., & Doursat, R. (2011). Emergent engineering: a radical paradigm shift.International Journal of Autonomous and Adaptive Communications Systems, 4(1), 39-60.

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  1. April 6, 2015

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