Cultural Algorithms. Robert G. Reynolds
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Название: Cultural Algorithms

Автор: Robert G. Reynolds

Издательство: John Wiley & Sons Limited

Жанр: Программы

Серия:

isbn: 9781119403104

isbn:

СКАЧАТЬ tend to send out agents to possibly high‐scoring predicted spots based on calculations made with known data.

      While explorative search suffers from covering massive amounts of ground with limited agents, exploitative suffers from a blindness brought on from agents that only focus on the immediate. With agents sharing information between knowledge sources, however, both types of knowledge source will benefit.

      Earlier it was mentioned that the homogeneous distribution of agents during the initialization phase of the runs gave a wide breadth of knowledge to the knowledge sources to use during each subsequent step. This could account for rapid early acquisition of a maximum. However, the dynamic landscape's violent landscape changes, which can result in clusters of agents suddenly being on a low‐scoring point in the landscape in unfamiliar terrain, show a more dramatic difference between the scores of the agents immediately after the shift, and several steps later when the maximum is reclaimed.

Image described by caption. Graphs of the KS fitnesses for the static and dynamic landscapes. Each graph has 5 curves for best topographical fitness, best situational fitness, best domain fitness, best normative fitness, and best historical fitness. Graph depicting the span of each Knowledge Source’s bounding boxes, with curves for “normative,” “situational,” “domain,” “history,” and “topographical.”

      Using this resulting information, it is possible to not only find the solution to a given problem but also to illustrate the in‐depth means by which the solution was found, and how each knowledge source contributed toward a given goal. It is due to this shared responsibility of the knowledge sources to both maintain acquired knowledge and push for the acquisition of new knowledge that the system maintains the balance between all of the knowledge sources as they each assert their influence over the collected individuals of the simulation.

      Despite being unable to visualize the data range, it is still possible to visualize the means in which the knowledge sources deal with the data they encounter. The additional optimization problems observed by the system include the designs of a Tension Spring, a Welded Beam, and a Pressure Vessel. Each problem sought to minimize the dimensions of a given structure to save on material and space, while still remaining within the constraints rendered necessary by factors, such as precision (for the shaping and rendering of parts) and safety (to reduce the likeliness of critical failure).

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      Source: Reproduced with permission of Elsevier.

Graph of KS fitnesses versus tick displaying five fluctuating curves for best topographical fitness, best situational fitness, best domain fitness, best normative fitness, and best historical 
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