Programming by optimization

By Holger H. Hoos. 2012

221 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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In Computational Theory and Mathematics

Citations in the corpus

Evolving Deep Neural Networks (2017, methodology): “stance, humans write the overall design of a soware system, and the parameters and low-level code is optimized automatically [23]; humans write imperfect versions of programs, and evolutionary algorithms are then used to repair them [15]; humans dene the space of possible web designs, and evolutio”
Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, methodology): “By making these choices explicit, Bayesian optimization can be used to construct optimal programs [74]: that is to say, programs that run faster or compute better solutions.”
The future of employment: How susceptible are jobs to computerisation? (2017, neutral): “For example, advances in ML allow a programmer to leave complex parameter and design choices to be appropriately optimised by an algorithm (Hoos, 2012).”
Bayesian Optimization in a Billion Dimensions via Random Embeddings (2013, background): “These successes have led to a paradigm shift in algo-rithm development towards the active design of highly parameterized frameworks that can be automatically customized to particular problem domains using optimization (Hoos, 2012; Bergstra et al., 2012).”
Discovering Gated Recurrent Neural Network Architectures (2018, methodology): “This has been true in VLSI design for a long time, but it has also become compelling in software engineering: The idea in "programming by optimization" is that humans should design only the framework and the details should be left for automated methods such as optimization (Hoos (2012)).”
Programming by Feedback (2014, background): “hard combinatorial problems, a new trend dubbed Programming by Optimization advocates algorithm portfolios endowed with a control layer such that determining what works best in a given use context [could be] performed automatically, substituting human labor by computation (Hoos, 2012).”
UvA-DARE (Digital Academic Repository) Bayesian Optimization in High Dimensions via Random Embeddings (2013, unknown): “eterized frameworks that can be automatically customized to particular problem domains using optimization [Hoos, 2012; Bergstra et al., 2012].”
Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem (2014, background): “In order to refrain from making any “premature commitment” (see Hoos, 2012), we consider all of them in the experimental analysis.”
The irace package: Iterated racing for automatic algorithm configuration (2016, methodology): “Nonetheless, the use of these methods has a crucial role in new ways of designing software, as advocated in the programming by optimization paradigm [40] .”
Algorithm Selection for Combinatorial Search Problems: A Survey (2012, methodology): “Related research explores how Algorithm Selection techniques can be used in the construction of software (Balasubramaniam et al., 2012; Hoos, 2012).”
Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models (2019, methodology): “Among these are the Programming by Optimization approach of Hoos (2012) and the Integrative Population Analysis approach of Glover et al. (1998).”
Automatically improving the anytime behaviour of optimisation algorithms (2014, methodology): “…performance of optimisation algorithms, when designing and evaluating new algorithms and when tuning existing algorithms to specific problems (Birattari, 2009; Bartz-Beielstein, 2006; Hutter, Hoos, Leyton-Brown, & Stützle, 2009; Eiben & Smit, 2011; Hutter, Hoos, & Leyton-Brown, 2011; Hoos, 2012).”