1,661 research outputs found
A MOS-based Dynamic Memetic Differential Evolution Algorithm for Continuous Optimization: A Scalability Test
Continuous optimization is one of the areas with more activity in the field of heuristic optimization. Many algorithms have been proposed and compared on several benchmarks of functions, with different performance depending on the problems. For this reason, the combination of different search strategies seems desirable to obtain the best performance of each of these approaches. This contribution explores the use of a hybrid memetic algorithm based on the multiple offspring framework. The proposed algorithm combines the explorative/exploitative strength of two heuristic search methods that separately obtain very competitive results. This algorithm has been tested with the benchmark problems and conditions defined for the special issue of the Soft Computing Journal on Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. The proposed algorithm obtained the best results compared with both its composing algorithms and a set of reference algorithms that were proposed for the special issue
Sequential vs. Integrated Algorithm Selection and Configuration: A Case Study for the Modular CMA-ES
When faced with a specific optimization problem, choosing which algorithm to
use is always a tough task. Not only is there a vast variety of algorithms to
select from, but these algorithms often are controlled by many hyperparameters,
which need to be tuned in order to achieve the best performance possible.
Usually, this problem is separated into two parts: algorithm selection and
algorithm configuration. With the significant advances made in Machine
Learning, however, these problems can be integrated into a combined algorithm
selection and hyperparameter optimization task, commonly known as the CASH
problem. In this work we compare sequential and integrated algorithm selection
and configuration approaches for the case of selecting and tuning the best out
of 4608 variants of the Covariance Matrix Adaptation Evolution Strategy
(CMA-ES) tested on the Black Box Optimization Benchmark (BBOB) suite. We first
show that the ranking of the modular CMA-ES variants depends to a large extent
on the quality of the hyperparameters. This implies that even a sequential
approach based on complete enumeration of the algorithm space will likely
result in sub-optimal solutions. In fact, we show that the integrated approach
manages to provide competitive results at a much smaller computational cost. We
also compare two different mixed-integer algorithm configuration techniques,
called irace and Mixed-Integer Parallel Efficient Global Optimization
(MIP-EGO). While we show that the two methods differ significantly in their
treatment of the exploration-exploitation balance, their overall performances
are very similar
ZOOpt: Toolbox for Derivative-Free Optimization
Recent advances of derivative-free optimization allow efficient approximating
the global optimal solutions of sophisticated functions, such as functions with
many local optima, non-differentiable and non-continuous functions. This
article describes the ZOOpt (https://github.com/eyounx/ZOOpt) toolbox that
provides efficient derivative-free solvers and are designed easy to use. ZOOpt
provides a Python package for single-thread optimization, and a light-weighted
distributed version with the help of the Julia language for Python described
functions. ZOOpt toolbox particularly focuses on optimization problems in
machine learning, addressing high-dimensional, noisy, and large-scale problems.
The toolbox is being maintained toward ready-to-use tool in real-world machine
learning tasks
Analysis of gameplay strategies in hearthstone: a data science approach
In recent years, games have been a popular test bed for AI research, and the presence of Collectible Card Games (CCGs) in that space is still increasing. One such CCG for both competitive/casual play and AI research is Hearthstone, a two-player adversarial game where players seeks to implement one of several gameplay strategies to defeat their opponent and decrease all of their Health points to zero. Although some open source simulators exist, some of their methodologies for simulated agents create opponents with a relatively low skill level. Using evolutionary algorithms, this thesis seeks to evolve agents with a higher skill level than those implemented in one such simulator, SabberStone. New benchmarks are propsed using supervised learning techniques to predict gameplay strategies from game data, and using unsupervised learning techniques to discover and visualize patterns that may be used in player modeling to differentiate gameplay strategies
A Population-based Hybrid Approach to Hyperparameter Optimization for Neural Networks
In recent years, large amounts of data have been generated, and computer
power has kept growing. This scenario has led to a resurgence in the interest
in artificial neural networks. One of the main challenges in training effective
neural network models is finding the right combination of hyperparameters to be
used. Indeed, the choice of an adequate approach to search the hyperparameter
space directly influences the accuracy of the resulting neural network model.
Common approaches for hyperparameter optimization are Grid Search, Random
Search, and Bayesian Optimization. There are also population-based methods such
as CMA-ES. In this paper, we present HBRKGA, a new population-based approach
for hyperparameter optimization. HBRKGA is a hybrid approach that combines the
Biased Random Key Genetic Algorithm with a Random Walk technique to search the
hyperparameter space efficiently. Several computational experiments on eight
different datasets were performed to assess the effectiveness of the proposed
approach. Results showed that HBRKGA could find hyperparameter configurations
that outperformed (in terms of predictive quality) the baseline methods in six
out of eight datasets while showing a reasonable execution time.Comment: 28 pages, 7 figure
Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants
Automated algorithm selection promises to support the user in the decisive
task of selecting a most suitable algorithm for a given problem. A common
component of these machine-trained techniques are regression models which
predict the performance of a given algorithm on a previously unseen problem
instance. In the context of numerical black-box optimization, such regression
models typically build on exploratory landscape analysis (ELA), which
quantifies several characteristics of the problem. These measures can be used
to train a supervised performance regression model.
First steps towards ELA-based performance regression have been made in the
context of a fixed-target setting. In many applications, however, the user
needs to select an algorithm that performs best within a given budget of
function evaluations. Adopting this fixed-budget setting, we demonstrate that
it is possible to achieve high-quality performance predictions with
off-the-shelf supervised learning approaches, by suitably combining two
differently trained regression models. We test this approach on a very
challenging problem: algorithm selection on a portfolio of very similar
algorithms, which we choose from the family of modular CMA-ES algorithms.Comment: To appear in Proc. of Genetic and Evolutionary Computation Conference
(GECCO'20
Model fusion using fuzzy aggregation: Special applications to metal properties
To improve the modelling performance, one should either propose a new modelling methodology or make the best of existing models. In this paper, the study is concentrated on the latter solution, where a structure-free modelling paradigm is proposed. It does not rely on a fixed structure and can combine various modelling techniques in ‘symbiosis’ using a ‘master fuzzy system’. This approach is shown to be able to include the advantages of different modelling techniques altogether by requiring less training and by minimising the efforts relating optimisation of the final structure. The proposed approach is then successfully applied to the industrial problems of predicting machining induced residual stresses for aerospace alloy components as well as modelling the mechanical properties of heat-treated alloy steels, both representing complex, non-linear and multi-dimensional environments
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