Cultural Algorithms. Robert G. Reynolds
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those produced with games.
As stated earlier, another feature of Cultural Algorithms is their ability to produce effective designs of networks for team‐based systems. The next two chapters focus specifically on team‐based design. Chapter 6 by Kobti et al. describes the use of Cultural Algorithms in the design of a variety of real‐world networks. They are interested in how CAs can be applied to the team formation problem, TFP. The TFP is in general NP‐hard. Efficient team formation is key to the success of large‐scale industry projects that employ a number of different individuals, each with their own expertise and skills. The first example used is a coauthorship network in which individuals collaborate to produce a specific product or outcome. A second example application is that of a palliative care network, where a team of healthcare providers are networked with each other and a number of patients. Cultural Algorithms are shown to be advantageous in each of these cases in comparison to traditional techniques in terms of producing efficient solutions to each of these different problems.
Chapter 7 by Ali et al. addresses the design of a competitive robot soccer team. It employs a population model based on Evolutionary Programming (EP) to evolve offensive and defensive strategies. First, Evolutionary Programming and Genetic Algorithms were each used to develop the offensive and defensive skills of a team. Next, EP was embedded into a CA as the population component. As it turned out, the CA enhanced the EP to beat both the unenhanced EP and GA teams as well as a hard‐coded default team. In other words, it was able to produce an increase in team performance beyond that of a human expert and was able to beat the unenhanced versions as well.
The following two chapters focus on the use of Cultural Algorithms in the solution of multiobjective problems. Chapter 8 by Kattan et al. employs Cultural Algorithms to assess the impact that climate change has on artisanal offshore fishing in Peru. Artisanal fishermen of the Pacific coast of Peru use traditional equipment to catch fish, unlike the large‐scale deep sea vessels. Marcus [8] collected the data for all fishing trips, over 6000, conducted from a specific coastal Peru port, Cerro Azul. During this period, the ecosystem was affected first by warming of the waters due to an El Nino, then by a subsequent cooling called La Nina, and finally a back to normal phase. A biobjective model of fishing behavior was produced that traded off quality catches versus investment in resources. On the one hand, a goal is to produce the highest payoff in terms of quality catches. On the other hand, since each fisherman is an independent producer, the goal is to minimize the resources needed to produce catches.
Pareto fronts were produced for each of the three phases and the results analyzed and compared statistically by a parallel Cultural Algorithm, CAPSO. CAPSO is short for Cultural Algorithm Particle Swarm Optimizer. The results indicate that the changes in the ecosystem produced by the warming and cooling of the regional waters resulted in statistically significant changes in the fishing behavior of the individual fishing agents. In the El Nino phase, individuals favored catch quality over resource investment. In the La Nina phase, they emphasized resource investment to insure producing any catch at all. The back to normal phase produced a more balanced set of fronts that represented the knowledge obtained over hundreds of year of fishing in the area, now that the requisite food chains were back to normal.
Chapter 9 by Stanley et al. describes the design of the parallel Cultural Algorithm, CAPSO. While CAPSO was designed initially to deal with the intensive parallelism inherent in the Peru Fishing computations, they were interested in how parallelism was actually needed to support the efficient solution of benchmark problems in multiobjective optimization. Often algorithms are tweaked to produce better results relative to existing benchmarks. That way, individuals can compare their approach with the solutions provided by other systems. This often results in algorithms that may be more tailored to the needs of the benchmark problems than those of the real world.
When CAPSO was applied to a representative set of benchmark problems, two basic patterns emerged. First, very little parallelism was needed to find an efficient solution of each of the problems. At most, around 30 parallel threads were needed as compared to the hundreds required for the fishing example. Second, the knowledge sources most frequently used to guide the search were exploitative in nature, rather than exploratory. The mathematical formulations of the examples were such that once explorations found a piece, the exploiter knowledge sources were able to fill in the rest. So the parallelism that was observed was primarily due to the exploration portion, which contributed to the overall computational time in a limited way.
Like Chapter 8, Chapter 10 also deals with cultural change. However, Chapter 8 dealt with relatively local and short‐term change, but the scale of change is markedly different in Chapter 10. The paper by Palazzolo et al. examines the use of Cultural Algorithms on a much larger scale project, one that involves hundreds of square miles and thousands of years. The Land Bridge project is an NSF supported cooperative project between the University of Michigan‐Ann Arbor and Wayne State University [9]. The project used AI technology to create a virtual world model of an ancient environment, now submerged under over a hundred feet of water in Lake Huron. The goal of the project is to predict the location of archaic hunting sites that existed over 10 000 years ago. This was done by producing a model of caribou behavior and using Cultural Algorithms to guide the production of optimal migration paths for large caribou herds across the ancient landscape; the assumption was that hunting sites and activities were positioned relative to these migration pathways. Cultural Algorithms were then employed to produce a system that used knowledge from the anthropological literature to predict hunting site location. The results were used to guide researchers to previously undiscovered sites.
References
1 1 Reynolds, R.G. (1999). An overview of Cultural Algorithms. In: Advances in Evolutionary Computation (eds. D. Corne, M. Dorigo and F. Glover), 367–378. New York: McGraw‐Hill.
2 2 Jayyousi, T.W. and Reynolds, R.G. (2014). Extracting urban occupational plans using cultural algorithms [application notes]. IEEE Computational Intelligence Magazine 9 (3): 66–87.
3 3 Reynolds, R.G. (1978). On modeling the evolution of hunter‐gatherer decision‐making systems. Geographical Analysis 10 (1): 31–46.
4 4 en.wikipedia.org Laws of Thermodynamics, 2020.
5 5 Reynolds, R.G. (2018). Culture on the Edge of Chaos. Springer.
6 6 Woodward, P. (ed.) (1957). Entropy and negentropy. IRE Transactions on Information Theory. 3 (1): 3–3.
7 7 Morrison R., De Jong K. (1999). A test problem generator for non‐stationary environments. Proceedings of the Congress on Evolutionary Computing, pp. 25–31.
8 8 Marcus, J. (ed.) (2016). Coastal Ecosystems and Economic Strategies at Cerro Azul, Peru. The study of a Late Intermediate Kingdom. Ann Arbor, MI: Memoirs of the Museum of Anthropology, University of Michigan.
9 9 O’Shea, J.M. (2002). The archaeology of scattered wreck‐sites: formation process and shallow water archaeology in western Lake Huron. The International Journal of the Nautical Archaeology 31 (2): 211–247.
Note
1 This work was supported by grant NSF #1744367.
2 The Cultural Algorithm Toolkit System
Thomas Palazzolo
Department