14,702 research outputs found
Free Search of real value or how to make computers think
This book introduces in detail Free Search - a novel advanced method for search and optimisation. It also deals with some essential questions that have been raised in a strong debate following the publication of this method in
journal and conference papers. In the light of this debate, Free Search deserves serious attention, as it appears to be superior to other competitive methods in the context of the experimental results obtained. This superiority is not only
quantitative in terms of the actual optimal value found but also qualitative in terms of independence from initial conditions and adaptation capabilities in an unknown environment
Algorithms Applied to Global Optimisation – Visual Evaluation
Evaluation and assessment of various search and optimisation algorithms is subject of large research efforts. Particular interest of this study is global optimisation and presented approach is based on observation and visual evaluation of Real-Coded Genetic Algorithm, Particle Swarm Optimisation, Differential Evolution and Free Search, which are briefly described and used for experiments. 3D graphical views, generated by visualisation tool VOTASA, illustrate essential aspects of global search process such as divergence, convergence, dependence on initialisation and utilisation of accidental events. Discussion on potential benefits of visual analysis, supported with numerical results, which could be used for comparative assessment of other methods and directions for further research conclude presented study
SQG-Differential Evolution for difficult optimization problems under a tight function evaluation budget
In the context of industrial engineering, it is important to integrate
efficient computational optimization methods in the product development
process. Some of the most challenging simulation-based engineering design
optimization problems are characterized by: a large number of design variables,
the absence of analytical gradients, highly non-linear objectives and a limited
function evaluation budget. Although a huge variety of different optimization
algorithms is available, the development and selection of efficient algorithms
for problems with these industrial relevant characteristics, remains a
challenge. In this communication, a hybrid variant of Differential Evolution
(DE) is introduced which combines aspects of Stochastic Quasi-Gradient (SQG)
methods within the framework of DE, in order to improve optimization efficiency
on problems with the previously mentioned characteristics. The performance of
the resulting derivative-free algorithm is compared with other state-of-the-art
DE variants on 25 commonly used benchmark functions, under tight function
evaluation budget constraints of 1000 evaluations. The experimental results
indicate that the new algorithm performs excellent on the 'difficult' (high
dimensional, multi-modal, inseparable) test functions. The operations used in
the proposed mutation scheme, are computationally inexpensive, and can be
easily implemented in existing differential evolution variants or other
population-based optimization algorithms by a few lines of program code as an
non-invasive optional setting. Besides the applicability of the presented
algorithm by itself, the described concepts can serve as a useful and
interesting addition to the algorithmic operators in the frameworks of
heuristics and evolutionary optimization and computing
Reset-free Trial-and-Error Learning for Robot Damage Recovery
The high probability of hardware failures prevents many advanced robots
(e.g., legged robots) from being confidently deployed in real-world situations
(e.g., post-disaster rescue). Instead of attempting to diagnose the failures,
robots could adapt by trial-and-error in order to be able to complete their
tasks. In this situation, damage recovery can be seen as a Reinforcement
Learning (RL) problem. However, the best RL algorithms for robotics require the
robot and the environment to be reset to an initial state after each episode,
that is, the robot is not learning autonomously. In addition, most of the RL
methods for robotics do not scale well with complex robots (e.g., walking
robots) and either cannot be used at all or take too long to converge to a
solution (e.g., hours of learning). In this paper, we introduce a novel
learning algorithm called "Reset-free Trial-and-Error" (RTE) that (1) breaks
the complexity by pre-generating hundreds of possible behaviors with a dynamics
simulator of the intact robot, and (2) allows complex robots to quickly recover
from damage while completing their tasks and taking the environment into
account. We evaluate our algorithm on a simulated wheeled robot, a simulated
six-legged robot, and a real six-legged walking robot that are damaged in
several ways (e.g., a missing leg, a shortened leg, faulty motor, etc.) and
whose objective is to reach a sequence of targets in an arena. Our experiments
show that the robots can recover most of their locomotion abilities in an
environment with obstacles, and without any human intervention.Comment: 18 pages, 16 figures, 3 tables, 6 pseudocodes/algorithms, video at
https://youtu.be/IqtyHFrb3BU, code at
https://github.com/resibots/chatzilygeroudis_2018_rt
Adaptive intelligence applied to numerical optimisation
The article presents modification strategies theoretical comparison and experimental results achieved by adaptive heuristics applied to numerical optimisation of several non-constraint test functions. The aims of the study are to identify and compare how adaptive search heuristics behave within heterogeneous search space without retuning of the search parameters. The achieved results are summarised and analysed, which could be used for comparison to other methods and further investigation
State-of-the-art in aerodynamic shape optimisation methods
Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles
We present a canonical way to turn any smooth parametric family of
probability distributions on an arbitrary search space into a
continuous-time black-box optimization method on , the
\emph{information-geometric optimization} (IGO) method. Invariance as a design
principle minimizes the number of arbitrary choices. The resulting \emph{IGO
flow} conducts the natural gradient ascent of an adaptive, time-dependent,
quantile-based transformation of the objective function. It makes no
assumptions on the objective function to be optimized.
The IGO method produces explicit IGO algorithms through time discretization.
It naturally recovers versions of known algorithms and offers a systematic way
to derive new ones. The cross-entropy method is recovered in a particular case,
and can be extended into a smoothed, parametrization-independent maximum
likelihood update (IGO-ML). For Gaussian distributions on , IGO
is related to natural evolution strategies (NES) and recovers a version of the
CMA-ES algorithm. For Bernoulli distributions on , we recover the
PBIL algorithm. From restricted Boltzmann machines, we obtain a novel algorithm
for optimization on . All these algorithms are unified under a
single information-geometric optimization framework.
Thanks to its intrinsic formulation, the IGO method achieves invariance under
reparametrization of the search space , under a change of parameters of the
probability distributions, and under increasing transformations of the
objective function.
Theory strongly suggests that IGO algorithms have minimal loss in diversity
during optimization, provided the initial diversity is high. First experiments
using restricted Boltzmann machines confirm this insight. Thus IGO seems to
provide, from information theory, an elegant way to spontaneously explore
several valleys of a fitness landscape in a single run.Comment: Final published versio
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