Reproductive cooperation between queens and their mated workers: the complex life history of an ant with a valuable nest.

The life history of Harpegnathos saltator is exceptional among ants because both queens and workers reproduce sexually. Recently mated queens start new colonies alone, but later some of the offspring workers also become inseminated and take over the egg-laying role. This alternation seems associated with the existence of very complex underground nests, which are designed to survive floods. Longevity of ponerine queens is low (a consequence of limited caste dimorphism in this "primitive" subfamily), and upon the death of an H. saltator foundress, the nest represents a substantial investment. The queen's progeny should thus be strongly selected to retain the valuable nests. Unlike the flying queens, the workers copulate with males from their own colonies, and, thus, their offspring are expected to be highly related to the foundress. Colony fission appears not to occur because a daughter fragment would lack an adequate nest for protection. Thus, the annual production of queens in colonies with reproductive workers remains essential for the establishment of new colonies. This contrasts with various other ponerine species in which the queens no longer exist.

Relatedness threshold for the production of female sexuals in colonies of a polygynous ant, Myrmica tahoensis, as revealed by microsatellite DNA analysis.

The genetic relationships of colony members in the ant Myrmica tahoensis were determined on the basis of highly polymorphic microsatellite DNA loci. These analyses show that colonies fall into one of two classes. In roughly half of the sampled colonies, workers and female offspring appear to be full sisters. The remaining colonies contain offspring produced by two or more queens. Colonies that produce female sexuals are always composed of highly related females, while colonies that produce males often show low levels of nestmate relatedness. These results support theoretical predictions that workers should skew sex allocation in response to relatedness asymmetries found within colonies. The existence of a relatedness threshold below which female sexuals are not produced suggests a possible mechanism for worker perception of relatedness. Two results indicate that workers use genetic cues, not queen number, in making sex-allocation decisions. (i) The number of queens in a colony was not significantly correlated with either the level of relatedness asymmetry or the sex ratio. (ii) Sex-ratio shifts consistent with a genetically based mechanism of relatedness assessment were seen in an experiment involving transfers of larvae among unrelated nests. Thus workers appear to make sex-allocation decisions on the basis of larval cues and appear to be able to adjust sex ratios long after egg laying.

An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering

This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, i.e. we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, i.e. an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (i.e. the ant-miner) reinforces nurse-rule pairs that receive higher rewards. A challenging real world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules.

An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering

This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, i.e. we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, i.e. an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (i.e. the ant-miner) reinforces nurse-rule pairs that receive higher rewards. A challenging real world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules.