346 research outputs found
Algorithm Portfolio for Individual-based Surrogate-Assisted Evolutionary Algorithms
Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation
tools for computationally expensive problems (CEPs). However, a randomly
selected algorithm may fail in solving unknown problems due to no free lunch
theorems, and it will cause more computational resource if we re-run the
algorithm or try other algorithms to get a much solution, which is more serious
in CEPs. In this paper, we consider an algorithm portfolio for SAEAs to reduce
the risk of choosing an inappropriate algorithm for CEPs. We propose two
portfolio frameworks for very expensive problems in which the maximal number of
fitness evaluations is only 5 times of the problem's dimension. One framework
named Par-IBSAEA runs all algorithm candidates in parallel and a more
sophisticated framework named UCB-IBSAEA employs the Upper Confidence Bound
(UCB) policy from reinforcement learning to help select the most appropriate
algorithm at each iteration. An effective reward definition is proposed for the
UCB policy. We consider three state-of-the-art individual-based SAEAs on
different problems and compare them to the portfolios built from their
instances on several benchmark problems given limited computation budgets. Our
experimental studies demonstrate that our proposed portfolio frameworks
significantly outperform any single algorithm on the set of benchmark problems
Efficient Quality Diversity Optimization of 3D Buildings through 2D Pre-optimization
Quality diversity algorithms can be used to efficiently create a diverse set
of solutions to inform engineers' intuition. But quality diversity is not
efficient in very expensive problems, needing 100.000s of evaluations. Even
with the assistance of surrogate models, quality diversity needs 100s or even
1000s of evaluations, which can make it use infeasible. In this study we try to
tackle this problem by using a pre-optimization strategy on a lower-dimensional
optimization problem and then map the solutions to a higher-dimensional case.
For a use case to design buildings that minimize wind nuisance, we show that we
can predict flow features around 3D buildings from 2D flow features around
building footprints. For a diverse set of building designs, by sampling the
space of 2D footprints with a quality diversity algorithm, a predictive model
can be trained that is more accurate than when trained on a set of footprints
that were selected with a space-filling algorithm like the Sobol sequence.
Simulating only 16 buildings in 3D, a set of 1024 building designs with low
predicted wind nuisance is created. We show that we can produce better machine
learning models by producing training data with quality diversity instead of
using common sampling techniques. The method can bootstrap generative design in
a computationally expensive 3D domain and allow engineers to sweep the design
space, understanding wind nuisance in early design phases.Comment: This is the final version and has been accepted for publication in
Evolutionary Computation (MIT Press
Combinatorial Surrogate-Assisted Optimization for Bus Stops Spacing Problem
International audienceThe distribution of transit stations constitutes an ubiquitous task in large urban areas. In particular, bus stops spacing is a crucial factor that directly affects transit ridership travel time. Hence, planners often rely on traffic surveys and virtual simulations of urban journeys to design sustainable public transport routes. However, the combinator-ial structure of the search space in addition to the time-consuming and black-box traffic simulations require computationally expensive efforts. This imposes serious constraints on the number of potential configurations to be explored. Recently, powerful techniques from discrete optimization and machine learning showed convincing to overcome these limitations. In this preliminary work, we build combinatorial surrogate models to approximate the costly traffic simulations. These so-trained surrog-ates are embedded in an optimization framework. More specifically, this article is the first to make use of a fresh surrogate-assisted optimization algorithm based on the mathematical foundations of discrete Walsh functions in order to solve the real-world bus stops spacing optimization problem. We conduct our experiments with the sialac benchmark in the city of Calais, France. We compare state-of-the-art approaches and we highlight the accuracy and the optimization efficiency of the proposed methods
Quality-diversity optimization: a novel branch of stochastic optimization
Traditional optimization algorithms search for a single global optimum that maximizes (or minimizes) the objective function. Multimodal optimization algorithms search for the highest peaks in the search space that can be more than one. Quality-Diversity algorithms are a recent addition to the evolutionary computation toolbox that do not only search for a single set of local optima, but instead try to illuminate the search space. In effect, they provide a holistic view of how high-performing solutions are distributed throughout a search space. The main differences with multimodal optimization algorithms are that (1) Quality-Diversity typically works in the behavioral space (or feature space), and not in the genotypic (or parameter) space, and (2) Quality-Diversity attempts to fill the whole behavior space, even if the niche is not a peak in the fitness landscape. In this chapter, we provide a gentle introduction to Quality-Diversity optimization, discuss the main representative algorithms, and the main current topics under consideration in the community. Throughout the chapter, we also discuss several successful applications of Quality-Diversity algorithms, including deep learning, robotics, and reinforcement learning
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