The EA paper I co-authored with Jason Ansel, Saman Amarasinghe and Una-May O’Reilly was accepted to GECCO 2011! The paper describes a novel Evolutionary Algorithm for solving incrementally structured, bottom-up problems. We evaluated our approach in the program autotuning context. Here’s the full abstract:

Many real world problems have a structure where small problem instances are embedded within large problem instances, or where solution quality for large problem instances is loosely correlated to that of small problem instances. This structure can be exploited because smaller problem instances typically have smaller search spaces and are cheaper to evaluate. We present an evolutionary algorithm, INCREA, which is designed to incrementally solve a large, noisy, computationally expensive problem by deriving its initial population through recursively running itself on problem instances of smaller sizes. The INCREA algorithm also expands and shrinks its population each generation and cuts off work that doesn’t appear to promise a fruitful result. For further efficiency, it addresses noisy solution quality efficiently by focusing on resolving it for small, potentially reusable solutions which have a much lower cost of evaluation. We compare INCREA to a general purpose evolutionary algorithm and find that in most cases INCREA arrives at the same solution in significantly less time.

The paper is available in full here:

An Efﬁcient Evolutionary Algorithm for Solving Incrementally Structured Problems

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