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Despite extensive research into the phenomenon of flow, there has been a comparative deficit in literature relating to the experience of shared or combined flow. This pilot study explored the subjective experience of combined flow in musical jam sessions, with particular emphasis on delineating the characteristics, outcomes, and practical applications unique to combined flow. In-depth semistructured interviews were held with six musicians who had extensive experience of group jam sessions. Grounded theory analysis of interview data identified two major themes; the experience of combined flow as a sequential progression through a set of stages; and the inter-subjectivity of the experience leading to the development of empathy between group members. A major finding was that the combined flow experience discussed by musicians met many of the criteria for classification as a flow experience, while also having the unique positive outcome of empathy development.

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It is sometimes claimed that genetic algorithms using diploid representations will be more suitable for problems in which the environment changes from time to time, as the additional information stored in the double chromosome will ensure diversity, which in turn allows the system to respond more quickly and robustly to a change in the fitness function. We have tested various diploid algorithms, with and without mechanisms for dominance change, on non-stationary problems, and conclude that some form of dominance change is essential, as a diploid encoding is not enough in itself to allow flexible response to change. Moreover, a haploid method which randomly mutates chromosomes whose fitness has fallen sharply also performs well on these problems.

We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and-conquer approach in which each heuristic solves a unique subset of the instance set considered. NELLI-GP extends an existing ensemble method called NELLI by introducing a novel heuristic generator that evolves heuristics composed of linear sequences of dispatching rules: each rule is represented using a tree structure and is itself evolved. Following a training period, the ensemble is shown to outperform both existing dispatching rules and a standard genetic programming algorithm on a large set of new test instances. In addition, it obtains superior results on a set of 210 benchmark problems from the literature when compared to two state-of-the-art hyper-heuristic approaches. Further analysis of the relationship between heuristics in the evolved ensemble and the instances each solves provides new insights into features that might describe similar instances.

The immune system is highly distributed, highly adaptive, self-organising

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