449 research outputs found
How Investors Can (and Can\u27t) Create Social Value
Most investors throughout the world have a single goal: to earn the highest risk- adjusted financial returns. They would not accept a lower financial return from an investment that also produced social benefits.
More recently, an increasing number of socially-motivated investors have goals beyond maximizing returns. They also seek to align their investments with their social values (value alignment), and some also seek to cause the companies in which they invest to create more social value as a result of their investment (social value creation). We show in this essay that while it is relatively easy to achieve value alignment, creating social value is far more difficult
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
Analyzing Adaptive Parameter Landscapes in Parameter Adaptation Methods for Differential Evolution
Since the scale factor and the crossover rate significantly influence the
performance of differential evolution (DE), parameter adaptation methods (PAMs)
for the two parameters have been well studied in the DE community. Although
PAMs can sufficiently improve the effectiveness of DE, PAMs are poorly
understood (e.g., the working principle of PAMs). One of the difficulties in
understanding PAMs comes from the unclarity of the parameter space that
consists of the scale factor and the crossover rate. This paper addresses this
issue by analyzing adaptive parameter landscapes in PAMs for DE. First, we
propose a concept of an adaptive parameter landscape, which captures a moment
in a parameter adaptation process. For each iteration, each individual in the
population has its adaptive parameter landscape. Second, we propose a method of
analyzing adaptive parameter landscapes using a 1-step-lookahead greedy
improvement metric. Third, we examine adaptive parameter landscapes in PAMs by
using the proposed method. Results provide insightful information about PAMs in
DE.Comment: This is an accepted version of a paper published in the proceedings
of GECCO 202
A Hybrid Global Minimization Scheme for Accurate Source Localization in Sensor Networks
We consider the localization problem of multiple wideband sources in a
multi-path environment by coherently taking into account the attenuation
characteristics and the time delays in the reception of the signal. Our
proposed method leaves the space for unavailability of an accurate signal
attenuation model in the environment by considering the model as an unknown
function with reasonable prior assumptions about its functional space. Such
approach is capable of enhancing the localization performance compared to only
utilizing the signal attenuation information or the time delays. In this paper,
the localization problem is modeled as a cost function in terms of the source
locations, attenuation model parameters and the multi-path parameters. To
globally perform the minimization, we propose a hybrid algorithm combining the
differential evolution algorithm with the Levenberg-Marquardt algorithm.
Besides the proposed combination of optimization schemes, supporting the
technical details such as closed forms of cost function sensitivity matrices
are provided. Finally, the validity of the proposed method is examined in
several localization scenarios, taking into account the noise in the
environment, the multi-path phenomenon and considering the sensors not being
synchronized
'Education, education, education' : legal, moral and clinical
This article brings together Professor Donald Nicolson's intellectual interest in professional legal ethics and his long-standing involvement with law clinics both as an advisor at the University of Cape Town and Director of the University of Bristol Law Clinic and the University of Strathclyde Law Clinic. In this article he looks at how legal education may help start this process of character development, arguing that the best means is through student involvement in voluntary law clinics. And here he builds upon his recent article which argues for voluntary, community service oriented law clinics over those which emphasise the education of students
A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms
The file attached to this record is the author's final peer reviewed version. The publisher's final version can be found by following the DOI link.The ensemble structure is a computational intelligence supervised strategy consisting of a pool of multiple operators that compete among each other for being selected, and an adaptation mechanism that tends to reward the most successful operators. In this paper we extend the idea of the ensemble to multiple local search logics. In a memetic fashion, the search structure of an ensemble framework cooperatively/competitively optimizes the problem jointly with a pool of diverse local search algorithms. In this way, the algorithm progressively adapts to a given problem and selects those search logics that appear to be the most appropriate to quickly detect high quality solutions. The resulting algorithm, namely Ensemble of Parameters and Strategies Differential Evolution empowered by Local Search (EPSDE-LS), is evaluated on multiple testbeds and dimensionality values. Numerical results show that the proposed EPSDE-LS robustly displays a very good performance in comparison with some of the state-of-the-art algorithms
Carotid Baroreflex Activation: Past, Present, and Future
Electrical activation of the carotid baroreceptor system is an attractive therapy for the treatment of resistant hypertension. In the past, several attempts were made to directly activate the baroreceptor system in humans, but the method had to be restricted to a few selected patients. Adverse effects, the need for better electrical devices and better surgical techniques, and the lack of knowledge about long-term effects has greatly hampered developments in this area for many years. Recently, a new and promising device was evaluated in a multicenter feasibility trial, which showed a clinically and statistically significant reduction in office systolic blood pressure (>20Â mm Hg). This reduction could be sustained for at least 2Â years with an acceptable safety profile. In the future, this new device may stimulate further application of electrical activation of the carotid baroreflex in treatment-resistant hypertension
A cloud-based enhanced differential evolution algorithm for parameter estimation problems in computational systems biology
This is a post-peer-review, pre-copyedit version of an article published in Cluster Computing. The final authenticated version is available online at: https://doi.org/10.1007/s10586-017-0860-1[Abstract] Metaheuristics are gaining increasing recognition in many research areas, computational systems biology among them. Recent advances in metaheuristics can be helpful in locating the vicinity of the global solution in reasonable computation times, with Differential Evolution (DE) being one of the most popular methods. However, for most realistic applications, DE still requires excessive computation times. With the advent of Cloud Computing effortless access to large number of distributed resources has become more feasible, and new distributed frameworks, like Spark, have been developed to deal with large scale computations on commodity clusters and cloud resources. In this paper we propose a parallel implementation of an enhanced DE using Spark. The proposal drastically reduces the execution time, by means of including a selected local search and exploiting the available distributed resources. The performance of the proposal has been thoroughly assessed using challenging parameter estimation problems from the domain of computational systems biology. Two different platforms have been used for the evaluation, a local cluster and the Microsoft Azure public cloud. Additionally, it has been also compared with other parallel approaches, another cloud-based solution (a MapReduce implementation) and a traditional HPC solution (a MPI implementation)Ministerio de EconomĂa y Competitividad; DPI2014-55276-C5-2-RMinisterio de EconomĂa y Competitividad; TIN2013-42148-PMinisterio de EconomĂa y Competitividad; TIN2016-75845-PXunta de Galicia ; R2016/045Xunta de Galicia; GRC2013/05
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