Advanced Computational Intelligence Paradigms in by Hiroyuki Yoshida, Ashlesha Jain, Ajita Ichalkaranje, Nikhil

By Hiroyuki Yoshida, Ashlesha Jain, Ajita Ichalkaranje, Nikhil Ichalkaranje

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For example, the top-down strategy starts with an initial population (loaded randomly or from the case-base) and goes on by evolving the weekly level for a given number of iteration steps (multi-level iteration step: 1). Then, the evolution proceeds on the level of daily plans (2) by evolving the attributes of the weekly menu plans. After the second level, the process continues on the level of meals (3), and after that, the evolution restarts from the weekly level (4). 2. In any strategy, there is no evolution on the level of dishes, nourishments, and nourishment components.

Social role awareness in animated agents. Autonomous Agents 5 (2001) 270–277 46. : Commentary: the challenge of training police officers. Journal of the American Academy of Psychiatry and the Law 33 (2005) 50–54 47. : Diagnosing attention disorders in a virtual classroom. IEEE Computer 37 (2004) 87–89 48. D. ): Foundations of augmented cognition. Mahwah, NJ: Lawrence Erlbaum Associates (2005) 49. : Computer games may be good for your health. Journal of Healthcare Information Management 16 (2002) 80–85 50.

Not surprisingly, we observed a strong linear connection between the probabilities of mutation and crossover, and runtime. However, our main concern is the quality of the solution. So we examined the connection between the runtime and the quality of the solution in a wide range of algorithmic setups (adjusted parameters were number of iteration, population size, probability of mutation and crossover). As Fig. 3 shows, although the quality of the solutions improves with longer runtimes (whether it is the effect of any of the adjusted parameters), the pace of improvement is very slow after a certain time, and, on the other hand, a solution of quite good quality is produced within this time.

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