Distributed Genetic Algorithms (DGAs) designed for the Internet have to take its high communication cost into consideration. For island model GAs, the migration topology has a major impact on DGA performance. This paper describes and evaluates an adaptive migration topology optimizer that keeps the communication load low while maintaining high solution quality. Experiments on benchmark problems show that the optimized topology outperforms static or random topologies of the same degree of connectivity. The applicability of the method on real-world problems is demonstrated on a hard optimization problem in VLSI design.
Whereas certain participants expressed a fear of falling which they managed by activity restriction, others described being confident in their balance despite avoidance of balance-challenging activities. Training was used as treatment to self-manage disease-related balance impairments in order to maintain independence in daily life. Implication for Rehabilitation People with Parkinson's disease require early advice about the positive effects of physical activity as well as strategies for self-management in order to ease the psychological and physical burden of progressive balance impairment. Fear of falling should be investigated alongside activity avoidance in this group in order to provide a more accurate insight into the scope of psychological concerns regarding balance and falls in everyday life. Certain people with Parkinson's disease define their balance according to activities they continue to participate in, while others who express fear-related activity avoidance require help to adapt balance-challenging activities in order to maintain balance confidence and avoid physical inactivity.
Deciding the appropriate population size and number of islands for distributed island-model genetic algorithms is often critical to the algorithm's success. This paper outlines a method that automatically searches for good combinations of island population sizes and the number of islands. The method is based on a race between competing parameter sets, and collaborative seeding of new parameter sets. This method is applicable to any problem, and makes distributed genetic algorithms easier to use by reducing the number of user-set parameters. The experimental results show that the proposed method robustly and reliably finds population and islands settings that are comparable to those found with traditional trial-and-error approaches.
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