# A framework for co-optimization algorithm performance and its application to worst-case optimization

@article{Popovici2015AFF, title={A framework for co-optimization algorithm performance and its application to worst-case optimization}, author={Elena Popovici and Ezra Winston}, journal={Theor. Comput. Sci.}, year={2015}, volume={567}, pages={46-73} }

## 9 Citations

Co-Optimization Free Lunches: Tractability of Optimal Black-Box Algorithms for Maximizing Expected Utility

- Computer ScienceEvolutionary Computation
- 2018

The design of generally well-performing black-box algorithms for expected-utility maximization problems is interested, that is, algorithms which have access to the utility function only via input–output queries.

Bridging Supervised Learning and Test-Based Co-optimization

- Computer ScienceJ. Mach. Learn. Res.
- 2017

This paper takes a close look at the important commonalities and subtle differences between the well-established field of supervised learning and the much younger one of cooptimization, providing a two-way dictionary for the respective terminologies used to describe these concepts.

A review of co-optimization approaches for operational and planning problems in the energy sector

- Computer ScienceApplied Energy
- 2021

Solving complex problems with coevolutionary algorithms

- BiologyGECCO Companion
- 2020

His research investigates the utility of coevolutionary methods under non-stationary environments, and uses coev evolution to facilitate the discovery of agents for reinforcement learning tasks in games such as the Arcade Learning Environment, VizDoom and Dota 2.

Adversarial genetic programming for cyber security: a rising application domain where GP matters

- Computer ScienceGenetic Programming and Evolvable Machines
- 2020

A framework called RIVALS is presented which supports the study of network security arms races and its goal is to elucidate the dynamics of cyber networks under attack by computationally modeling and simulating them.

Solving Complex Problems with Coevolutionary Algorithms

- Computer ScienceGECCO
- 2016

The work of Krzysztof Krawiec includes problem decomposition using cooperative coevolution, learning strategies for Othello, Go, and other games using competetive CoEAs, and discovery of underlying objectives in test-based problems.

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