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No free lunch theorems for optimization
A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of "no free lunch" (NFL) theorems are presented which
No Free Lunch Theorems for Search
We show that all algorithms that search for an extremum of a cost function perform exactly the same, when averaged over all possible cost functions. In particular, if algorithm A outperforms
A practical heuristic for finding graph minors
We present a heuristic algorithm for finding a graph $H$ as a minor of a graph $G$ that is practical for sparse $G$ and $H$ with hundreds of vertices. We also explain the practical importance of
Coevolutionary free lunches
  • D. Wolpert, W. Macready
  • Mathematics, Computer Science
    IEEE Transactions on Evolutionary Computation
  • 1 December 2005
TLDR
This paper presents a general framework covering most optimization scenarios and shows that in self-play there are free lunches: in coevolution some algorithms have better performance than other algorithms, averaged across all possible problems.
A Robust Learning Approach to Domain Adaptive Object Detection
TLDR
A robust object detection framework that is resilient to noise in bounding box class labels, locations and size annotations is proposed that significantly improves the state-of-the-art on multiple domain adaptation scenarios on the SIM10K, Cityscapes and KITTI datasets.
The Ising model : teaching an old problem new tricks
In this paper we investigate the use of hardware which physically realizes quantum annealing for machine learning applications. We show how to take advantage of the hardware in both zeroand
Discrete optimization using quantum annealing on sparse Ising models
TLDR
A way of finding energy representations with large classical gaps between ground and first excited states, efficient algorithms for mapping non-compatible Ising models into the hardware, and the use of decomposition methods for problems that are too large to fit in hardware are proposed.
Search strategies for applied molecular evolution.
TLDR
This work utilizes a spin-glass-like model, the NK model, to analyze search strategies based on pooling, mutation, recombination and selective hill-climbing, and suggests that pooling followed by recombinated molecules finds better candidate molecules than pooling alone on most molecular landscapes.
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