48 research outputs found
Challenges of ELA-guided Function Evolution using Genetic Programming
Within the optimization community, the question of how to generate new
optimization problems has been gaining traction in recent years. Within topics
such as instance space analysis (ISA), the generation of new problems can
provide new benchmarks which are not yet explored in existing research. Beyond
that, this function generation can also be exploited for solving complex
real-world optimization problems. By generating functions with similar
properties to the target problem, we can create a robust test set for algorithm
selection and configuration.
However, the generation of functions with specific target properties remains
challenging. While features exist to capture low-level landscape properties,
they might not always capture the intended high-level features. We show that a
genetic programming (GP) approach guided by these exploratory landscape
analysis (ELA) properties is not always able to find satisfying functions. Our
results suggest that careful considerations of the weighting of landscape
properties, as well as the distance measure used, might be required to evolve
functions that are sufficiently representative to the target landscape
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
Per-run Algorithm Selection with Warm-starting using Trajectory-based Features
Per-instance algorithm selection seeks to recommend, for a given problem
instance and a given performance criterion, one or several suitable algorithms
that are expected to perform well for the particular setting. The selection is
classically done offline, using openly available information about the problem
instance or features that are extracted from the instance during a dedicated
feature extraction step. This ignores valuable information that the algorithms
accumulate during the optimization process.
In this work, we propose an alternative, online algorithm selection scheme
which we coin per-run algorithm selection. In our approach, we start the
optimization with a default algorithm, and, after a certain number of
iterations, extract instance features from the observed trajectory of this
initial optimizer to determine whether to switch to another optimizer. We test
this approach using the CMA-ES as the default solver, and a portfolio of six
different optimizers as potential algorithms to switch to. In contrast to other
recent work on online per-run algorithm selection, we warm-start the second
optimizer using information accumulated during the first optimization phase. We
show that our approach outperforms static per-instance algorithm selection. We
also compare two different feature extraction principles, based on exploratory
landscape analysis and time series analysis of the internal state variables of
the CMA-ES, respectively. We show that a combination of both feature sets
provides the most accurate recommendations for our test cases, taken from the
BBOB function suite from the COCO platform and the YABBOB suite from the
Nevergrad platform
Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games
The term Procedural Content Generation (PCG) refers to the (semi-)automatic
generation of game content by algorithmic means, and its methods are becoming
increasingly popular in game-oriented research and industry. A special class of
these methods, which is commonly known as search-based PCG, treats the given
task as an optimisation problem. Such problems are predominantly tackled by
evolutionary algorithms.
We will demonstrate in this paper that obtaining more information about the
defined optimisation problem can substantially improve our understanding of how
to approach the generation of content. To do so, we present and discuss three
efficient analysis tools, namely diagonal walks, the estimation of high-level
properties, as well as problem similarity measures. We discuss the purpose of
each of the considered methods in the context of PCG and provide guidelines for
the interpretation of the results received. This way we aim to provide methods
for the comparison of PCG approaches and eventually, increase the quality and
practicality of generated content in industry.Comment: 30 pages, 8 figures, accepted for publication in Applied Soft
Computin