688 research outputs found
How Good are Genetic Algorithms at Finding Large Cliques: An Experimental Study
This paper investigates the power of genetic algorithms at solving the MAX-CLIQUE problem. We measure the performance of a standard genetic algorithm on an elementary set of problem instances consisting of embedded cliques in random graphs. We indicate the need for improvement, and introduce a new genetic algorithm, the multi-phase annealed GA, which exhibits superior performance on the same problem set.
As we scale up the problem size and test on \hard" benchmark instances, we notice a
degraded performance in the algorithm caused by premature convergence to local minima. To alleviate this problem, a sequence of modi cations are implemented ranging from changes in input representation to systematic local search. The most recent version, called union GA, incorporates the features of union cross-over, greedy replacement, and diversity enhancement. It shows a marked speed-up in the number of iterations required to find a given solution, as well as some improvement in the clique size found.
We discuss issues related to the SIMD implementation of the genetic algorithms on a Thinking Machines CM-5, which was necessitated by the intrinsically high time complexity (O(n3)) of the serial algorithm for computing one iteration.
Our preliminary conclusions are: (1) a genetic algorithm needs to be heavily customized to work "well" for the clique problem; (2) a GA is computationally very expensive, and its use is only recommended if it is known to find larger cliques than other algorithms; (3) although our customization e ort is bringing forth continued improvements, there is no clear evidence, at this time, that a GA will have better success in circumventing local minima.NSF (CCR-9204284
Multi-threading a state-of-the-art maximum clique algorithm
We present a threaded parallel adaptation of a state-of-the-art maximum clique
algorithm for dense, computationally challenging graphs. We show that near-linear speedups
are achievable in practice and that superlinear speedups are common. We include results for
several previously unsolved benchmark problems
An Efficient Bandit Algorithm for Realtime Multivariate Optimization
Optimization is commonly employed to determine the content of web pages, such
as to maximize conversions on landing pages or click-through rates on search
engine result pages. Often the layout of these pages can be decoupled into
several separate decisions. For example, the composition of a landing page may
involve deciding which image to show, which wording to use, what color
background to display, etc. Such optimization is a combinatorial problem over
an exponentially large decision space. Randomized experiments do not scale well
to this setting, and therefore, in practice, one is typically limited to
optimizing a single aspect of a web page at a time. This represents a missed
opportunity in both the speed of experimentation and the exploitation of
possible interactions between layout decisions.
Here we focus on multivariate optimization of interactive web pages. We
formulate an approach where the possible interactions between different
components of the page are modeled explicitly. We apply bandit methodology to
explore the layout space efficiently and use hill-climbing to select optimal
content in realtime. Our algorithm also extends to contextualization and
personalization of layout selection. Simulation results show the suitability of
our approach to large decision spaces with strong interactions between content.
We further apply our algorithm to optimize a message that promotes adoption of
an Amazon service. After only a single week of online optimization, we saw a
21% conversion increase compared to the median layout. Our technique is
currently being deployed to optimize content across several locations at
Amazon.com.Comment: KDD'17 Audience Appreciation Awar
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