Analysis of the (μ/μI,λ)(\mu/\mu_I,\lambda)-CSA-ES with Repair by Projection Applied to a Conically Constrained Problem

Theoretical analyses of evolution strategies are indispensable for gaining a deep understanding of their inner workings. For constrained problems, rather simple problems are of interest in the current research. This work presents a theoretical analysis of a multi-recombinative evolution strategy with cumulative step size adaptation applied to a conically constrained linear optimization problem. The state of the strategy is modeled by random variables and a stochastic iterative mapping is introduced. For the analytical treatment, fluctuations are neglected and the mean value iterative system is considered. Non-linear difference equations are derived based on one-generation progress rates. Based on that, expressions for the steady state of the mean value iterative system are derived. By comparison with real algorithm runs, it is shown that for the considered assumptions, the theoretical derivations are able to predict the dynamics and the steady state values of the real runs.Comment: This is a PREPRINT of an article that has been accepted for publication in the journal MIT Press Evolutionary Computation (ECJ). 25 pages + supplementary material. The work was supported by the Austrian Science Fund FWF under grant P29651-N3

On the performance of (1,l)-Evolution Strategies at the ridge function class

This paper presents the N-dependent analysis of the (1, l) Evolution Strategy (ES) with isotropic mutations at the ridge functions including the special cases sharp and parabolic ridge. The new approach presented allows for the prediction of the dynamics in ridge direction as well as in radial direction. The central quantities are the corresponding progress rates which are determined in terms of analytical expressions. Its predictive quality is evaluated by ES simulations and the steady state behavior is discussed in detail

Assessing factors affecting cross-border M&A versus Greenfield FDI : a cross country analysis

Mestrado Bolonha em FinançasMultinational Enterprises can expand their operation to foreign markets using two main investment approaches: cross-border M&A and greenfield FDI. Due to the lack of sizeable empirical literature, the following study investigates and compares the main determinants driving cross-border M&A and greenfield FDI in selected European, Asian, and North American countries. The paper empirically analyzes the extent to which economic, labor force, institutional-political, and monetary-financial variables influence the choice of foreign establishment. The study applies three empirical methods, the ordinary least squares, random effects, and fixed effects, to the panel data of 70 countries. We find a robust and positive relationship between GDP and FDI of both modes in developing countries and a negative association in Asian developed and transitioning countries. Additionally, the labor force participation rate negatively affects cross-border M&A in developing countries. In contrast, the inflation rate provides evidence of a positive effect on greenfield FDI in Asian, European, and North American countries.As multinacionais podem expandir a sua operação para mercados externos utilizando duas principais abordagens de investimento: IDE por operações de F&A transfronteiriças e IDE pela via de projetos de raiz (greenfield). Atendendo à literatura existente, o presente estudo investiga e compara os principais determinantes que conduzem a F&A transfronteiriças ou a IDE em formato greenfield num conjunto de países europeus, asiáticos e norte-americanos. O estudo analisa empiricamente até que ponto as variáveis económicas, laborais, institucionais-políticas e de política monetária influenciam a escolha do formato de IDE. O estudo aplica três métodos empíricos a dados de painel de 70 países, o do método dos mínimos quadrados, efeitos aleatórios e efeitos fixos. Encontramos uma relação robusta e positiva entre o PIB e o IDE de ambas as formas, nos países em desenvolvimento, e uma associação negativa nos países asiáticos desenvolvidos e em transição. Além disso, a taxa de participação da mão-de-obra afeta negativamente as F&A transfronteiriças nos países em desenvolvimento. Por outro lado, os resultados sugerem que a taxa de inflação apresenta uma associação positiva com o IDE por greenfield nos países asiáticos, europeus e norte-americanos.info:eu-repo/semantics/publishedVersio

Self-Adaptive Genetic Algorithms with Simulated Binary Crossover

Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored only with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self-adaptive feature of real-parameter genetic algorithms (GAs) using simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of self-adaptive ESs and real-parameter GAs with SBX operator is also discussed. Thereafter, the self-adaptive behavior of real-parameter GAs is demonstrated on a number of test problems commonly-used in the ES literature. The remarkable similarity in the working principle of real-parameter GAs and self-adaptive ESs shown in this study suggests the need of emphasizing further studies on self-adaptive GAs

On the Analysis of Self-Adaptive Evolutionary Algorithms

Due to the exibility in adapting to different fitness landscapes, self-adaptive evolutionary algorithms (SA-EAs) have been gaining popularity in the recent past. In this paper, we postulate the properties that SA-EA operators should have for successful applications. Specifically, population mean and variance of a number of SA-EA operators, such as various real-parameter crossover operators and self-adaptive evolution strategies, are calculated for this purpose. In every case, simulation results are shown to verify the theoretical calculations. The postulations and population variance calculations explain why self-adaptive GAs and ESs have shown similar performance in the past and also suggest appropriate strategy parameter values which must be chosen while applying and comparing different SA-EAs

Benchmarking Evolutionary Algorithms For Single Objective Real-valued Constrained Optimization - A Critical Review

Benchmarking plays an important role in the development of novel search algorithms as well as for the assessment and comparison of contemporary algorithmic ideas. This paper presents common principles that need to be taken into account when considering benchmarking problems for constrained optimization. Current benchmark environments for testing Evolutionary Algorithms are reviewed in the light of these principles. Along with this line, the reader is provided with an overview of the available problem domains in the field of constrained benchmarking. Hence, the review supports algorithms developers with information about the merits and demerits of the available frameworks.Comment: This manuscript is a preprint version of an article published in Swarm and Evolutionary Computation, Elsevier, 2018. Number of pages: 4

Optimum Tracking with Evolution Strategies

Evolutionary algorithms are frequently applied to dynamic optimization problems in which the objective varies with time. It is desirable to gain an improved understanding of the influence of different genetic operators and of the parameters of a strategy on its tracking performance. An approach that has proven useful in the past is to mathematically analyze the strategy's behavior in simple, idealized environments. The present paper investigates the performance of a multiparent evolution strategy that employs cumulative step length adaptation for an optimization task in which the target moves linearly with uniform speed. Scaling laws that quite accurately describe the behavior of the strategy and that greatly contribute to its understanding are derived. It is shown that in contrast to previously obtained results for a randomly moving target, cumulative step length adaptation fails to achieve optimal step lengths if the target moves in a linear fashion. Implications for the choice of population size parameters are discussed

Local Performance of the (1 + 1)-ES in a Noisy Environment

While noise is a phenomenon present in many real-world optimization problems, the understanding of its potential effects on the performance of evolutionary algorithms is still incomplete. This paper investigates the effects of noise for the infinite-dimensional quadratic sphere and a (1 +1)-ES with isotropic normal mutations. It is shown that overvaluation as a result of failure to reevaluate parental fitness leads to both reduced success probabilities and improved performance. Implications for mutation strength adaptation rules are discussed and optimal resampling rates are computed