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S. Kobayashi - One of the best experts on this subject based on the ideXlab platform.

  • Discussions of Worker Ants' rule-based CHC dealing with changing environments
    Applied Soft Computing, 2010
    Co-Authors: A. Kamiya, Kazuya Abiko, S. Kobayashi
    Abstract:

    Contrary to popular belief, biologists discovered that Worker Ants are really not all hardworking. It has been found that in three separate 30-strong colonies of black Japanese Ants (Myrmecina nipponica), about 20% of Worker Ants are diligent, 60% are ordinary, and 20% are lazy. That is called 20:60:20 rule. Though they are lazy, biologists suggested that lazy Worker Ants could be contributing something to the colony that is yet to be determined. In our last research, we used CHC (cross generational elitist selection, heterogeneous recombination, and cataclysmic mutation) with the Worker Ants' rule (WACHC) aiming at solving optimization problems in changing environments. CHC is a nontraditional genetic algorithm (GA) which combines a conservative selection strategy that always preserves the best individuals found so far with a radical (highly disruptive) recombination operator. In our last research, we verified that WACHC performs better than CHC in only one case of fully changing environment. In this paper, we further discuss our proposed WACHC dealing with changing environment problems with varying degree of difficulty, compare our proposal with hypermutation GA which is also proposed for dealing with changing environment problems, and discuss the difference between our proposal and ant colony optimization algorithms.

  • Worker Ants' rule-based genetic algorithms dealing with changing environments
    Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications 2005. SMCia 05., 1
    Co-Authors: A. Kamiya, F. Makino, S. Kobayashi
    Abstract:

    Contrary to popular belief, biologists discovered that Worker Ants are really not all hardworking. It has been found that in three separate 30-strong colonies of black Japanese Ants (Myrmecina nipponica), about 20% of Worker Ants are diligent, 60% are ordinary, and 20% are lazy. That is called 20:60:20 rule. Though they are lazy, biologists suggested that lazy Worker Ants could be contributing something to the colony that is yet to be determined. This paper verified that genetic algorithms (GAs) with this Worker Ants' rule can solve an artificial ant problem efficiently in changing environments. In our approach, for each generation, we preserve not only individuals of high fitness but also individuals of low fitness. As a result of simulation conducted in a changing environment, the best performance of our proposed GA was obtained when the number of preserved individuals of high fitness and low fitness are each close to 20% of the population, while the remaining nearly 60% individuals are created by genetic operations, namely, crossover and mutation. This simulation result reinforces the 20:60:20 rule discovered in nature ant colonies. In a changing environment, this simulation result also indicates that Worker Ants' rule-based GA outperforms simple GA and CHC.

Kobayashishigenobu - One of the best experts on this subject based on the ideXlab platform.

A. Kamiya - One of the best experts on this subject based on the ideXlab platform.

  • Discussions of Worker Ants' rule-based CHC dealing with changing environments
    Applied Soft Computing, 2010
    Co-Authors: A. Kamiya, Kazuya Abiko, S. Kobayashi
    Abstract:

    Contrary to popular belief, biologists discovered that Worker Ants are really not all hardworking. It has been found that in three separate 30-strong colonies of black Japanese Ants (Myrmecina nipponica), about 20% of Worker Ants are diligent, 60% are ordinary, and 20% are lazy. That is called 20:60:20 rule. Though they are lazy, biologists suggested that lazy Worker Ants could be contributing something to the colony that is yet to be determined. In our last research, we used CHC (cross generational elitist selection, heterogeneous recombination, and cataclysmic mutation) with the Worker Ants' rule (WACHC) aiming at solving optimization problems in changing environments. CHC is a nontraditional genetic algorithm (GA) which combines a conservative selection strategy that always preserves the best individuals found so far with a radical (highly disruptive) recombination operator. In our last research, we verified that WACHC performs better than CHC in only one case of fully changing environment. In this paper, we further discuss our proposed WACHC dealing with changing environment problems with varying degree of difficulty, compare our proposal with hypermutation GA which is also proposed for dealing with changing environment problems, and discuss the difference between our proposal and ant colony optimization algorithms.

  • Worker Ants' rule-based genetic algorithms dealing with changing environments
    Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications 2005. SMCia 05., 1
    Co-Authors: A. Kamiya, F. Makino, S. Kobayashi
    Abstract:

    Contrary to popular belief, biologists discovered that Worker Ants are really not all hardworking. It has been found that in three separate 30-strong colonies of black Japanese Ants (Myrmecina nipponica), about 20% of Worker Ants are diligent, 60% are ordinary, and 20% are lazy. That is called 20:60:20 rule. Though they are lazy, biologists suggested that lazy Worker Ants could be contributing something to the colony that is yet to be determined. This paper verified that genetic algorithms (GAs) with this Worker Ants' rule can solve an artificial ant problem efficiently in changing environments. In our approach, for each generation, we preserve not only individuals of high fitness but also individuals of low fitness. As a result of simulation conducted in a changing environment, the best performance of our proposed GA was obtained when the number of preserved individuals of high fitness and low fitness are each close to 20% of the population, while the remaining nearly 60% individuals are created by genetic operations, namely, crossover and mutation. This simulation result reinforces the 20:60:20 rule discovered in nature ant colonies. In a changing environment, this simulation result also indicates that Worker Ants' rule-based GA outperforms simple GA and CHC.

Katsuya Ichinose - One of the best experts on this subject based on the ideXlab platform.

  • seasonal variation in nestmate recognition in paratrechina flavipes smith Worker Ants hymenoptera formicidae
    Animal Behaviour, 1991
    Co-Authors: Katsuya Ichinose
    Abstract:

    Abstract Colonies of Paratrechina flavipes were split into artificial nests, and placed at three different sites. About every 2 weeks for 2 years Workers from one of these nests were paired with (related) Workers from another nest from the same colony or with (unrelated) Workers from a different colony. Aggression between Workers of the paired nests varied seasonally. Workers were aggressive to related individuals only during the season when the nest was active, but they were always aggressive to unrelated Workers. Such variability in aggression within a colony may be due to a seasonal change in the way nestmates are recognized, i.e. Workers in active nests may discriminate their nestmates from non-nestmates by using both environmental and genetic odours, whereas when the nest is inactive they use genetic odour alone. The variable recognition mechanism of this ant may be involved in the evolution of more than one nest and one reproductive queen in a colony.

Kamiyaakimoto - One of the best experts on this subject based on the ideXlab platform.

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