History: Scaling up

Scaling Up

Scaling Up

Most analytical and experimental results on evolutionary programming (EP) are obtained using low-dimensional problems, e.g., smaller than 50. It is unclear, however, whether the empirical results obtained from the low-dimensional problems still hold for high-dimensional cases. This paper investigates the behaviour of four diierent EP algorithms for large-scale problems, i.e., problems whose dim...

Scaling - Up or Out

Insatiable demands for performance and the availability of inexpensive microprocessors have led to the development of multiprocessor-based systems. Passionate debates amongst architects, designers, and vendors continue to rage regarding cost effective multiprocessor design alternatives. Hypes and claims of performance and scalability are confusing and often misleading. Increasing performance an...

Scaling up co-design

In this paper we reflect on our experiences in a project where academic researchers and social change organizations are working together to explore how participatory and co-design practices can be disseminated and spread within the ‘third sector’. The research project is itself co-designed and co-produced, but within various constraints arising from research funding models. We explore both our ...

Scaling Up and Evaluation

This paper discusses the key role that impact evaluations should play in scaling up. Credible impact evaluations are needed to ensure that the most effective programs are scaled up at the national or international levels. Scaling up is possible only if a case can be made that programs that have been successful on small scale would work in other contexts. Therefore, the very objective of scaling...