77,586 research outputs found
A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems
Copyright @ Springer-Verlag 2008Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant Nos. 70431003 and 70671020, the National Innovation Research Community Science Foundation of China under Grant No. 60521003, and the National Support Plan of China under Grant No. 2006BAH02A09 and the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01
Locally Adaptive Optimization: Adaptive Seeding for Monotone Submodular Functions
The Adaptive Seeding problem is an algorithmic challenge motivated by
influence maximization in social networks: One seeks to select among certain
accessible nodes in a network, and then select, adaptively, among neighbors of
those nodes as they become accessible in order to maximize a global objective
function. More generally, adaptive seeding is a stochastic optimization
framework where the choices in the first stage affect the realizations in the
second stage, over which we aim to optimize.
Our main result is a -approximation for the adaptive seeding
problem for any monotone submodular function. While adaptive policies are often
approximated via non-adaptive policies, our algorithm is based on a novel
method we call \emph{locally-adaptive} policies. These policies combine a
non-adaptive global structure, with local adaptive optimizations. This method
enables the -approximation for general monotone submodular functions
and circumvents some of the impossibilities associated with non-adaptive
policies.
We also introduce a fundamental problem in submodular optimization that may
be of independent interest: given a ground set of elements where every element
appears with some small probability, find a set of expected size at most
that has the highest expected value over the realization of the elements. We
show a surprising result: there are classes of monotone submodular functions
(including coverage) that can be approximated almost optimally as the
probability vanishes. For general monotone submodular functions we show via a
reduction from \textsc{Planted-Clique} that approximations for this problem are
not likely to be obtainable. This optimization problem is an important tool for
adaptive seeding via non-adaptive policies, and its hardness motivates the
introduction of \emph{locally-adaptive} policies we use in the main result
Interactive Chemical Reactivity Exploration
Elucidating chemical reactivity in complex molecular assemblies of a few
hundred atoms is, despite the remarkable progress in quantum chemistry, still a
major challenge. Black-box search methods to find intermediates and
transition-state structures might fail in such situations because of the
high-dimensionality of the potential energy surface. Here, we propose the
concept of interactive chemical reactivity exploration to effectively introduce
the chemist's intuition into the search process. We employ a haptic pointer
device with force-feedback to allow the operator the direct manipulation of
structures in three dimensions along with simultaneous perception of the
quantum mechanical response upon structure modification as forces. We elaborate
on the details of how such an interactive exploration should proceed and which
technical difficulties need to be overcome. All reactivity-exploration concepts
developed for this purpose have been implemented in the Samson programming
environment.Comment: 36 pages, 14 figure
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