This paper discusses optimization of functions with uncertainty by means of Genetic Algorithms(GA). For such problems, there have been proposed methods of sampling fitness values several times and taking average of them for evaluation of each individual. However, in important applications having uncertain fitness functions such as online adaptation of real systems and optimization through complicated computer simulation using random variables, possible number of fitness evaluation is quite limited. Hence, methods achieving optimization with less number of fitness evaluation is needed. In the present paper, the authors propose a GA utilizing history of search (Memory-based Fitness Evaluation GA: MFEGA) so as to reduce the number of fitness evaluation for such applications of GA. In the MFEGA, value of fitness function at a novel search point is estimated not only by the sampled fitness value at that point but also by utilizing the fitness values of individuals stored in the history of search. Numerical experiments show that the proposed method outperforms the conventional GA of sampling fitness values several times at each search point in noisy environment.
Insects are capable of extremely rapid collision avoidance behaviors with a minimum of processing overhead. These features make them interacting for robotics. To implement the strategies of insect in mobile robots or cars, a through evaluation of these systems is necessary. We developed a closed-loop experimental system for analyzing insect collision avoidance behavior in virtual environments. In the current implementation, a tethered female cricket walks on a floating sphere and the rotations of the sphere are translated into movements of a "virtual cricket" in a computergenerated virtual space. Visual information including obstacle and background patterns in the virtual space are then fed back to the tethered cricket as visual stimuli projected onto a screen in front of the cricket. To induce reliably reproducible straight-line walking, we applied the male calling song that induces positive phonotaxis in female crickets. We demonstrated that the tethered female cricket displayed collision avoidance behavior in response to visual stimuli during positive phonotaxis. By employing this system, we investigated a key stimulus that triggered collision avoidance behavior in crickets in different behavioral contexts: a cricket approaching a static object and an object moving towards a quiescent cricket. The results indicate that crickets used a certain threshold of image size (visual angle) of the projected object as a key stimulus. Furthermore, we found that the threshold depended on the behavioral context: quiescent crickets started avoidance farther from the approaching object compared to crickets walking towards a static object. We conclude that behavioral context is an important factor in decision making. With our closed-loop system for behavioral analysis, we can systematically extract the conditions under which optimal behavioral performance is obtained. This will be an important step in the design of sensory processors for robots.
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