123 research outputs found
Niching an estimation-of-distribution algorithm by hierarchical Gaussian mixture learning
Estimation-of-Distribution Algorithms (EDAs) have been applied with quite some success when solving real-valued optimization problems, especially in the case of Black Box Optimization (BBO). Generally, the performance of an EDA depends on the match between its driving probability distribution and the landscape of the problem being solved. Because most well-known EDAs, including CMA-ES, NES, and AMaLGaM, use a uni-modal search distribution, they have a high risk of getting trapped in local optima when a problem is multi-modal with a (moderate) number of relatively comparable modes. This risk could potentially be mitigated using niching methods that define multiple regions of interest where separate search distributions govern sub-populations. However, a key question is how to determine a suitable number of niches, especially in BBO. In this paper, we present a novel, adaptive niching approach that determines the niches through hierarchical clustering based on the correlation between the probability densities and fitness values of solutions. We test the performance of a combination of this niching approach with AMaLGaM on both new and well-known niching benchmark problems and ind that the new approach properly identifies multiple landscape modes, leading to much beter performance on multi-modal problems than with a non-niched, uni-modal EDA
Discovering the Elite Hypervolume by Leveraging Interspecies Correlation
Evolution has produced an astonishing diversity of species, each filling a
different niche. Algorithms like MAP-Elites mimic this divergent evolutionary
process to find a set of behaviorally diverse but high-performing solutions,
called the elites. Our key insight is that species in nature often share a
surprisingly large part of their genome, in spite of occupying very different
niches; similarly, the elites are likely to be concentrated in a specific
"elite hypervolume" whose shape is defined by their common features. In this
paper, we first introduce the elite hypervolume concept and propose two metrics
to characterize it: the genotypic spread and the genotypic similarity. We then
introduce a new variation operator, called "directional variation", that
exploits interspecies (or inter-elites) correlations to accelerate the
MAP-Elites algorithm. We demonstrate the effectiveness of this operator in
three problems (a toy function, a redundant robotic arm, and a hexapod robot).Comment: In GECCO 201
Learning to Generate Genotypes with Neural Networks
Neural networks and evolutionary computation have a rich intertwined history. They most commonly appear together when an evolutionary algorithm optimises the parameters and topology of a neural network for reinforcement learning problems, or when a neural network is applied as a surrogate fitness function to aid the evolutionary optimisation of expensive fitness functions. In this paper we take a different approach, asking the question of whether a neural network can be used to provide a mutation distribution for an evolutionary algorithm, and what advantages this approach may offer? Two modern neural network models are investigated, a Denoising Autoencoder modified to produce stochastic outputs and the Neural Autoregressive Distribution Estimator. Results show that the neural network approach to learning genotypes is able to solve many difficult discrete problems, such as MaxSat and HIFF, and regularly outperforms other evolutionary techniques
MATEDA: A suite of EDA programs in Matlab
This paper describes MATEDA-2.0, a suite of programs in Matlab for
estimation of distribution algorithms. The package allows the optimization of single and multi-objective problems with estimation of distribution
algorithms (EDAs) based on undirected graphical models and Bayesian
networks. The implementation is conceived for allowing the incorporation
by the user of different combinations of selection, learning, sampling, and
local search procedures. Other included methods allow the analysis of the
structures learned by the probabilistic models, the visualization of particular features of these structures and the use of the probabilistic models
as fitness modeling tools
Stochastic and deterministic algorithms for continuous black-box optimization
Continuous optimization is never easy: the exact solution
is always a luxury demand and the theory of it is not always analytical and
elegant. Continuous optimization, in practice, is essentially about the
efficiency: how to obtain the solution with same quality using as minimal
resources (e.g., CPU time or memory usage) as possible? In this thesis, the
number of function evaluations is considered as the most important resource
to save. To achieve this goal, various efforts have been implemented and
applied successfully. One research stream focuses on the so-called stochastic
variation (mutation) operator, which conducts an (local) exploration of the
search space. The efficiency of those operator has been investigated closely,
which shows a good stochastic variation should be able to generate a good
coverage of the local neighbourhood around the current search solution. This
thesis contributes on this issue by formulating a novel stochastic variation
that yields good space coverage.
Algorithms and the Foundations of Software technolog
Uncertainty evaluation of reservoir simulation models using particle swarms and hierarchical clustering
History matching production data in finite difference reservoir simulation
models has been and always will be a challenge for the industry. The
principal hurdles that need to be overcome are finding a match in the first
place and more importantly a set of matches that can capture the uncertainty
range of the simulation model and to do this in as short a time as possible
since the bottleneck in this process is the length of time taken to run the
model. This study looks at the implementation of Particle Swarm
Optimisation (PSO) in history matching finite difference simulation models.
Particle Swarms are a class of evolutionary algorithms that have shown
much promise over the last decade. This method draws parallels from the
social interaction of swarms of bees, flocks of birds and shoals of fish.
Essentially a swarm of agents are allowed to search the solution hyperspace
keeping in memory each individual’s historical best position and iteratively
improving the optimisation by the emergent interaction of the swarm. An
intrinsic feature of PSO is its local search capability. A sequential niching
variation of the PSO has been developed viz. Flexi-PSO that enhances the
exploration and exploitation of the hyperspace and is capable of finding
multiple minima. This new variation has been applied to history matching
synthetic reservoir simulation models to find multiple distinct history
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matches to try to capture the uncertainty range. Hierarchical clustering is
then used to post-process the history match runs to reduce the size of the
ensemble carried forward for prediction.
The success of the uncertainty modelling exercise is then assessed by
checking whether the production profile forecasts generated by the ensemble
covers the truth case
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