8,686 research outputs found
Unbiased Black-Box Complexities of Jump Functions
We analyze the unbiased black-box complexity of jump functions with small,
medium, and large sizes of the fitness plateau surrounding the optimal
solution.
Among other results, we show that when the jump size is , that is, only a small constant fraction of the fitness values
is visible, then the unbiased black-box complexities for arities and higher
are of the same order as those for the simple \textsc{OneMax} function. Even
for the extreme jump function, in which all but the two fitness values
and are blanked out, polynomial-time mutation-based (i.e., unary unbiased)
black-box optimization algorithms exist. This is quite surprising given that
for the extreme jump function almost the whole search space (all but a
fraction) is a plateau of constant fitness.
To prove these results, we introduce new tools for the analysis of unbiased
black-box complexities, for example, selecting the new parent individual not by
comparing the fitnesses of the competing search points, but also by taking into
account the (empirical) expected fitnesses of their offspring.Comment: This paper is based on results presented in the conference versions
[GECCO 2011] and [GECCO 2014
Reducing the Arity in Unbiased Black-Box Complexity
We show that for all the -ary unbiased black-box
complexity of the -dimensional \onemax function class is . This
indicates that the power of higher arity operators is much stronger than what
the previous bound by Doerr et al. (Faster black-box algorithms
through higher arity operators, Proc. of FOGA 2011, pp. 163--172, ACM, 2011)
suggests.
The key to this result is an encoding strategy, which might be of independent
interest. We show that, using -ary unbiased variation operators only, we may
simulate an unrestricted memory of size bits.Comment: An extended abstract of this paper has been accepted for inclusion in
the proceedings of the Genetic and Evolutionary Computation Conference (GECCO
2012
Complexity Theory for Discrete Black-Box Optimization Heuristics
A predominant topic in the theory of evolutionary algorithms and, more
generally, theory of randomized black-box optimization techniques is running
time analysis. Running time analysis aims at understanding the performance of a
given heuristic on a given problem by bounding the number of function
evaluations that are needed by the heuristic to identify a solution of a
desired quality. As in general algorithms theory, this running time perspective
is most useful when it is complemented by a meaningful complexity theory that
studies the limits of algorithmic solutions.
In the context of discrete black-box optimization, several black-box
complexity models have been developed to analyze the best possible performance
that a black-box optimization algorithm can achieve on a given problem. The
models differ in the classes of algorithms to which these lower bounds apply.
This way, black-box complexity contributes to a better understanding of how
certain algorithmic choices (such as the amount of memory used by a heuristic,
its selective pressure, or properties of the strategies that it uses to create
new solution candidates) influences performance.
In this chapter we review the different black-box complexity models that have
been proposed in the literature, survey the bounds that have been obtained for
these models, and discuss how the interplay of running time analysis and
black-box complexity can inspire new algorithmic solutions to well-researched
problems in evolutionary computation. We also discuss in this chapter several
interesting open questions for future work.Comment: This survey article is to appear (in a slightly modified form) in the
book "Theory of Randomized Search Heuristics in Discrete Search Spaces",
which will be published by Springer in 2018. The book is edited by Benjamin
Doerr and Frank Neumann. Missing numbers of pointers to other chapters of
this book will be added as soon as possibl
Axiomatic Interpretability for Multiclass Additive Models
Generalized additive models (GAMs) are favored in many regression and binary
classification problems because they are able to fit complex, nonlinear
functions while still remaining interpretable. In the first part of this paper,
we generalize a state-of-the-art GAM learning algorithm based on boosted trees
to the multiclass setting, and show that this multiclass algorithm outperforms
existing GAM learning algorithms and sometimes matches the performance of full
complexity models such as gradient boosted trees.
In the second part, we turn our attention to the interpretability of GAMs in
the multiclass setting. Surprisingly, the natural interpretability of GAMs
breaks down when there are more than two classes. Naive interpretation of
multiclass GAMs can lead to false conclusions. Inspired by binary GAMs, we
identify two axioms that any additive model must satisfy in order to not be
visually misleading. We then develop a technique called Additive
Post-Processing for Interpretability (API), that provably transforms a
pre-trained additive model to satisfy the interpretability axioms without
sacrificing accuracy. The technique works not just on models trained with our
learning algorithm, but on any multiclass additive model, including multiclass
linear and logistic regression. We demonstrate the effectiveness of API on a
12-class infant mortality dataset.Comment: KDD 201
Editorial for the Special Issue on Combinatorial Optimization Problems
First paragraph: In combinatorial optimization, the goal is to find an optimal solution, according to some objective function, from a discrete search space. These problems arise widely in industry and academia and, unfortunately, many of them are NP-hard and no polynomial time algorithm can guarantee their solution to a certified optimality unless. Therefore, in the last decades researchers have investigated the use of stochastic search algorithms to find near optimal solutions to these problems. In particular, great research efforts have been devoted to the development and application of metaheuristic algorithms to solve combinatorial optimization problems
Black Box Adversarial Prompting for Foundation Models
Prompting interfaces allow users to quickly adjust the output of generative
models in both vision and language. However, small changes and design choices
in the prompt can lead to significant differences in the output. In this work,
we develop a black-box framework for generating adversarial prompts for
unstructured image and text generation. These prompts, which can be standalone
or prepended to benign prompts, induce specific behaviors into the generative
process, such as generating images of a particular object or generating high
perplexity text
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