Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Optimizing for reduced code space using genetic algorithms
Proceedings of the ACM SIGPLAN 1999 workshop on Languages, compilers, and tools for embedded systems
Using SWIG to Bind C++ to Python
Computing in Science and Engineering
Crossover in Grammatical Evolution: The Search Continues
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Grammatical Evolution: Evolving Programs for an Arbitrary Language
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Meta optimization: improving compiler heuristics with machine learning
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Combined Selection of Tile Sizes and Unroll Factors Using Iterative Compilation
PACT '00 Proceedings of the 2000 International Conference on Parallel Architectures and Compilation Techniques
ACME: adaptive compilation made efficient
LCTES '05 Proceedings of the 2005 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
Fast compiler optimisation evaluation using code-feature based performance prediction
Proceedings of the 4th international conference on Computing frontiers
Evaluating Heuristic Optimization Phase Order Search Algorithms
Proceedings of the International Symposium on Code Generation and Optimization
Genetic programming applied to compiler heuristic optimization
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Data movement optimisation in point-free form
AMAST'06 Proceedings of the 11th international conference on Algebraic Methodology and Software Technology
A framework for point-free program transformation
IFL'05 Proceedings of the 17th international conference on Implementation and Application of Functional Languages
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Optimising compilers present their authors with an intractable design space. A substantial body of work has used heuristic search techniques to search this space for the purposes of adapting optimisers to their environment. To date, most of this work has focused on sequencing, tuning and guiding the actions of atomic hand-written optimisation phases. In this paper we explore the adaption of optimisers at a deeper level by demonstrating that it is feasible to automatically build a non-trivial optimisation phase, for a simple functional language, using Grammatical Evolution. We show that the individuals evolved compare well in performance to a handwritten optimisation phase on a range of benchmarks. We conclude with proposals of how this work and its applications can be extended.