22,047 research outputs found
Fitness Uniform Optimization
In evolutionary algorithms, the fitness of a population increases with time
by mutating and recombining individuals and by a biased selection of more fit
individuals. The right selection pressure is critical in ensuring sufficient
optimization progress on the one hand and in preserving genetic diversity to be
able to escape from local optima on the other hand. Motivated by a universal
similarity relation on the individuals, we propose a new selection scheme,
which is uniform in the fitness values. It generates selection pressure toward
sparsely populated fitness regions, not necessarily toward higher fitness, as
is the case for all other selection schemes. We show analytically on a simple
example that the new selection scheme can be much more effective than standard
selection schemes. We also propose a new deletion scheme which achieves a
similar result via deletion and show how such a scheme preserves genetic
diversity more effectively than standard approaches. We compare the performance
of the new schemes to tournament selection and random deletion on an artificial
deceptive problem and a range of NP-hard problems: traveling salesman, set
covering and satisfiability.Comment: 25 double-column pages, 12 figure
Explaining Adaptation in Genetic Algorithms With Uniform Crossover: The Hyperclimbing Hypothesis
The hyperclimbing hypothesis is a hypothetical explanation for adaptation in
genetic algorithms with uniform crossover (UGAs). Hyperclimbing is an
intuitive, general-purpose, non-local search heuristic applicable to discrete
product spaces with rugged or stochastic cost functions. The strength of this
heuristic lie in its insusceptibility to local optima when the cost function is
deterministic, and its tolerance for noise when the cost function is
stochastic. Hyperclimbing works by decimating a search space, i.e. by
iteratively fixing the values of small numbers of variables. The hyperclimbing
hypothesis holds that UGAs work by implementing efficient hyperclimbing. Proof
of concept for this hypothesis comes from the use of a novel analytic technique
involving the exploitation of algorithmic symmetry. We have also obtained
experimental results that show that a simple tweak inspired by the
hyperclimbing hypothesis dramatically improves the performance of a UGA on
large, random instances of MAX-3SAT and the Sherrington Kirkpatrick Spin
Glasses problem.Comment: 22 pages, 5 figure
On the landscape of combinatorial optimization problems
This paper carries out a comparison of the fitness landscape for four classic optimization problems: Max-Sat, graph-coloring, traveling salesman, and quadratic assignment. We have focused on two types of properties, local average properties of the landscape, and properties of the local optima. For the local optima we give a fairly comprehensive description of the properties, including the expected time to reach a local optimum, the number of local optima at different cost levels, the distance between optima, and the expected probability of reaching the optima. Principle component analysis is used to understand the correlations between the local optima. Most of the properties that we examine have not been studied previously, particularly those concerned with properties of the local optima. We compare and contrast the behavior of the four different problems. Although the problems are very different at the low level, many of the long-range properties exhibit a remarkable degree of similarity
Estimating meme fitness in adaptive memetic algorithms for combinatorial problems
Among the most promising and active research areas in heuristic optimisation is the field of adaptive memetic algorithms (AMAs). These gain much of their reported robustness by adapting the probability with which each of a set of local improvement operators is applied, according to an estimate of their current value to the search process. This paper addresses the issue of how the current value should be estimated. Assuming the estimate occurs over several applications of a meme, we consider whether the extreme or mean improvements should be used, and whether this aggregation should be global, or local to some part of the solution space. To investigate these issues, we use the well-established COMA framework that coevolves the specification of a population of memes (representing different local search algorithms) alongside a population of candidate solutions to the problem at hand. Two very different memetic algorithms are considered: the first using adaptive operator pursuit to adjust the probabilities of applying a fixed set of memes, and a second which applies genetic operators to dynamically adapt and create memes and their functional definitions. For the latter, especially on combinatorial problems, credit assignment mechanisms based on historical records, or on notions of landscape locality, will have limited application, and it is necessary to estimate the value of a meme via some form of sampling. The results on a set of binary encoded combinatorial problems show that both methods are very effective, and that for some problems it is necessary to use thousands of variables in order to tease apart the differences between different reward schemes. However, for both memetic algorithms, a significant pattern emerges that reward based on mean improvement is better than that based on extreme improvement. This contradicts recent findings from adapting the parameters of operators involved in global evolutionary search. The results also show that local reward schemes outperform global reward schemes in combinatorial spaces, unlike in continuous spaces. An analysis of evolving meme behaviour is used to explain these findings. © 2012 by the Massachusetts Institute of Technology
Reinforcement Learning in Different Phases of Quantum Control
The ability to prepare a physical system in a desired quantum state is
central to many areas of physics such as nuclear magnetic resonance, cold
atoms, and quantum computing. Yet, preparing states quickly and with high
fidelity remains a formidable challenge. In this work we implement cutting-edge
Reinforcement Learning (RL) techniques and show that their performance is
comparable to optimal control methods in the task of finding short,
high-fidelity driving protocol from an initial to a target state in
non-integrable many-body quantum systems of interacting qubits. RL methods
learn about the underlying physical system solely through a single scalar
reward (the fidelity of the resulting state) calculated from numerical
simulations of the physical system. We further show that quantum state
manipulation, viewed as an optimization problem, exhibits a spin-glass-like
phase transition in the space of protocols as a function of the protocol
duration. Our RL-aided approach helps identify variational protocols with
nearly optimal fidelity, even in the glassy phase, where optimal state
manipulation is exponentially hard. This study highlights the potential
usefulness of RL for applications in out-of-equilibrium quantum physics.Comment: A legend for the videos referred to in the paper is available on
https://mgbukov.github.io/RL_movies
Towards the Inferrence of Structural Similarity of Combinatorial Landscapes
One of the most common problem-solving heuristics is by analogy. For a given
problem, a solver can be viewed as a strategic walk on its fitness landscape.
Thus if a solver works for one problem instance, we expect it will also be
effective for other instances whose fitness landscapes essentially share
structural similarities with each other. However, due to the black-box nature
of combinatorial optimization, it is far from trivial to infer such similarity
in real-world scenarios. To bridge this gap, by using local optima network as a
proxy of fitness landscapes, this paper proposed to leverage graph data mining
techniques to conduct qualitative and quantitative analyses to explore the
latent topological structural information embedded in those landscapes. By
conducting large-scale empirical experiments on three classic combinatorial
optimization problems, we gain concrete evidence to support the existence of
structural similarity between landscapes of the same classes within neighboring
dimensions. We also interrogated the relationship between landscapes of
different problem classes
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Combinatorial optimization and metaheuristics
Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods
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