Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Optimizing for reduced code space using genetic algorithms
Proceedings of the ACM SIGPLAN 1999 workshop on Languages, compilers, and tools for embedded systems
Evolving neural networks through augmenting topologies
Evolutionary Computation
Finding effective optimization phase sequences
Proceedings of the 2003 ACM SIGPLAN conference on Language, compiler, and tool for embedded systems
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
LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
Finding effective compilation sequences
Proceedings of the 2004 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
Automatic Selection of Compiler Options Using Non-parametric Inferential Statistics
Proceedings of the 14th International Conference on Parallel Architectures and Compilation Techniques
Using Machine Learning to Focus Iterative Optimization
Proceedings of the International Symposium on Code Generation and Optimization
Exhaustive Optimization Phase Order Space Exploration
Proceedings of the International Symposium on Code Generation and Optimization
Automatic generation of peephole superoptimizers
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
Evaluating Heuristic Optimization Phase Order Search Algorithms
Proceedings of the International Symposium on Code Generation and Optimization
Cole: compiler optimization level exploration
Proceedings of the 6th annual IEEE/ACM international symposium on Code generation and optimization
Evolutionary reinforcement learning of artificial neural networks
International Journal of Hybrid Intelligent Systems - Hybridization of Intelligent Systems
DXNN: evolving complex organisms in complex environments using a novel tweann system
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Mitigating the compiler optimization phase-ordering problem using machine learning
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
Handbook of Neuroevolution Through Erlang
Handbook of Neuroevolution Through Erlang
Proceedings of the eighteenth international conference on Architectural support for programming languages and operating systems
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There is a complex web of interactions between optimization phases in static program compilation. Because there are many different types of optimizations, and each changes the form of the program and can impact the result of subsequent optimizations, the selection of optimizations to apply is challenging and is known as the "optimization phase ordering problem." There is a need to effectively optimize the order of the optimizations and specific optimizations used based on the statistics and other features of the program to gain the most benefit. In this work we propose the use of evolved neural networks to intelligently choose which optimizations are applied and in what order, to a method or a program as a whole, based on its features. In this paper we study the use of the memetic algorithm-based neuroevolutionary system called DXNN, and a genetic algorithm based neuroevolutionary system called NEAT, to evolve such neural networks.