@inproceedings {Simula.simula.1480,
	title = {The GISMOE Challenge: Constructing the Pareto Program Surface Using Genetic Programming to Find Better Programs},
	journal = {IEEE/ACM International Conference On Automated Software Engineering (ASE)},
	year = {2012},
	publisher = {IEEE/ACM},
	type = {Conference},
	address = {.},
	abstract = {Optimising programs for non-functional properties such as speed, size, throughput, power consumption and bandwidth can be demanding; pity the poor programmer who is asked to cater for them all at once! We set out an alternate vi- sion for a new kind of software development environment inspired by recent results from Search Based Software Engi- neering (SBSE). Given an input program that satises the functional requirements, the proposed programming envi- ronment will automatically generate a set of candidate pro- gram implementations, all of which share functionality, but each of which di er in their non-functional trade o s. The software designer navigates this diverse Pareto surface of candidate implementations, gaining insight into the trade o s and selecting solutions for di erent platforms and en- vironments, thereby stretching beyond the reach of current compiler technologies. Rather than having to focus on the details required to manage complex, inter-related and con- icting, non-functional trade o s, the designer is thus freed to explore, to understand, to control and to decide rather than to construct.},
	author = {Harman, Mark and Langdon, William and Jia, Yue and White, David and Arcuri, Andrea and Clark, John}
}
