Diversity as a Necessity for Sustainability in Cultural Systems: Collective Problem-Solving in Cultural Algorithms

Cultural Algorithms have led to the development of many ways to distribute information within social networks. These mechanisms act by helping the system make decisions about how information is distributed through a population network, and thus are called distribution or decision mechanisms. Many distribution mechanisms have been developed using techniques from auction theory, game theory and various forms of voting construct. In this paper a new extension of a system called Subcultures is described. Previous forms of the Subcultures system involved allowing Knowledge Sources within the Belief Space to choose what network was used for their distribution in the Population Space based on the complexity of the problem at hand. Here the Subcultures system is extended to allow the selection of distribution mechanisms along with the network. The new Subcultured distribution mechanism is compared with the results of each individual distribution mechanism without a subculture, when applied to a series of dynamic complex optimization problems of varying complexities. The results suggest that relatively simple mechanism such as Weighted Majority Vote and First Price Auction are sufficient for environments that exhibit low entropic levels of change such as in linear environments. For non-linearly changing environments, English Auctions and Sub-Cultures are most of effective. For the most chaotic environments, the sub-cultured approach was the most effective of the two. What these results suggest that while voting approaches work well in predictably changing environments, cultural diversity is a necessity for sustainability in an environment that is changing nonlinearly. This information can be used by a human technician in the adjustment of the Cultural Algorithms during its operation over an extended period of time as the complexity of the environment changes.

[1]  Christopher G. Langton,et al.  Life at the Edge of Chaos , 1992 .

[2]  Robert G. Reynolds,et al.  The Impact of Subcultures in Cultural Algorithm Problem Solving , 2015, 2015 IEEE Symposium Series on Computational Intelligence.

[3]  Robert G. Reynolds Cultural Algorithm Framework , 2018 .

[4]  Robert G. Reynolds,et al.  Problem solving using social networks in Cultural Algorithms with auctions , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[5]  R.W. Morrison,et al.  A test problem generator for non-stationary environments , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[6]  Robert G. Reynolds,et al.  Optimization problem solving with auctions in Cultural Algorithms , 2013, Memetic Comput..

[7]  Robert G. Reynolds,et al.  Population mechanics and cultural algorithms in the development of a cultural engine , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

[8]  R.G. Reynolds,et al.  Computing with the social fabric: The evolution of social intelligence within a cultural framework , 2008, IEEE Computational Intelligence Magazine.

[9]  Robert G. Reynolds,et al.  Using Common Value Auction In Cultural Algorithm to Enhance Robustness and Resilience of Social Knowledge Distribution Systems , 2018, 2018 IEEE Symposium Series on Computational Intelligence (SSCI).

[10]  Robert G. Reynolds,et al.  Optimizing AI Pipelines: A Game-Theoretic Cultural Algorithms Approach , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[11]  Robert G. Reynolds,et al.  Robust evolution optimization at the edge of chaos: Commercialization of culture algorithms , 2010, IEEE Congress on Evolutionary Computation.