Effects of among-offspring relatedness on the origins and evolution of parental care and filial cannibalism.

Parental care is expected to increase the likelihood of offspring survival at the cost of investment in future reproductive success. However, alternative parental behaviours, such as filial cannibalism, can decrease current reproductive success and consequently individual fitness. We evaluate the role of among-offspring relatedness on the evolution of parental care and filial cannibalism. Building on our previous work, we show how the evolution of care is influenced by the effect of among-offspring relatedness on both the strength of competition and filial cannibalism. When there is a positive relationship between among-offspring competition and relatedness, parental care will be favoured when among-offspring relatedness is relatively low, and the maintenance of both care and no-care strategies is expected. If the relationship between among-offspring competition and relatedness is negative, parental care is most strongly favoured when broods contain highly related offspring. Further, we highlight the range of conditions over which the level of this among-offspring relatedness can affect the co-occurrence of different care/no care and cannibalism/no cannibalism strategies. Coexistence of multiple strategies is independent of the effects of among-offspring relatedness on cannibalism but more likely when among-offspring relatedness and competition are positively associated

A Hyper-arc Consistency Algorithm for the Soft Alldifferent Constraint

This paper presents an algorithm that achieves hyper-arc consistency for the soft alldifferent constraint. To this end, we prove and exploit the equivalence with a minimum-cost flow problem. Consistency of the constraint can be checked in O(nm) time, and hyper-arc consistency is achieved in O(m) time, where n is the number of variables involved and m is the sum of the cardinalities of the domains. It improves a previous method that did not ensure hyper-arc consistency

Collaborative Learning for Constraint Solving

Although constraint programming offers a wealth of strong, general-purpose methods, in practice a complex, real application demands a person who selects, combines, and refines various available techniques for constraint satisfaction and optimization

The Adaptive Constraint Engine

The Adaptive Constraint Engine (ACE) seeks to automate the application of constraint programming expertise and the extraction of domain-specific expertise. Under the aegis of FORR, an architecture for learning and problemsolving, ACE learns search-order heuristics from problem solving experience