Adaptation, Evolution, and Archaeological Phases: Some Implications of Reynolds' Simulation

2021 ◽  
pp. 501-507
Author(s):  
Kent V. Flannery
Keyword(s):  
Adaptation Evolution

scholarly journals A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications

2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
Csaba Balázs ◽  
◽  
Melissa van Beekveld ◽  
Sascha Caron ◽  
Barry M. Dillon ◽  
...  
Keyword(s):  
Random Sampling ◽  
Detailed Comparison ◽  
Global Optimisation ◽  
Adaptive Memory ◽  
Test Functions ◽  
Bee Colony ◽  
Covariance Matrix Adaptation ◽  
Global Fit ◽  
Adaptation Evolution ◽  
Optimisation Algorithms

Abstract Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensive to evaluate. We describe a number of global optimisation algorithms that are not yet widely used in particle astrophysics, benchmark them against random sampling and existing techniques, and perform a detailed comparison of their performance on a range of test functions. These include four analytic test functions of varying dimensionality, and a realistic example derived from a recent global fit of weak-scale supersymmetry. Although the best algorithm to use depends on the function being investigated, we are able to present general conclusions about the relative merits of random sampling, Differential Evolution, Particle Swarm Optimisation, the Covariance Matrix Adaptation Evolution Strategy, Bayesian Optimisation, Grey Wolf Optimisation, and the PyGMO Artificial Bee Colony, Gaussian Particle Filter and Adaptive Memory Programming for Global Optimisation algorithms.

Optimization Design of Trusses Based on Covariance Matrix Adaptation Evolution Strategy Algorithm

2012 ◽  
Vol 215-216 ◽  
pp. 133-137
Author(s):  
Guo Shao Su ◽  
Yan Zhang ◽  
Zhen Xing Wu ◽  
Liu Bin Yan
Keyword(s):  
Stochastic Optimization ◽  
Covariance Matrix ◽  
Optimization Problems ◽  
Fitness Function ◽  
Optimization Methods ◽  
Evolution Strategy ◽  
Covariance Matrix Adaptation ◽  
Study Results ◽  
Highly Nonlinear ◽  
Adaptation Evolution

Covariance matrix adaptation evolution strategy algorithm (CMA-ES) is a newly evolution algorithm. It has become a powerful tool for solving highly nonlinear multi-peak optimization problems. In many real-world optimization problems, the location of multiple optima is often required in a search space. In order to evaluate the solution, thousands of fitness function evaluations are involved that is a time consuming or expensive processes. Therefore, conventional stochastic optimization methods meet a special challenge for a very large number of problem function evaluations. Aiming to overcome the shortcoming of stochastic optimization methods in the high calculation cost, a truss optimal method based on CMA-ES algorithm is proposed and applied to solve the section and shape optimization problems of trusses. The study results show that the method is feasible and has the advantages of high accuracy, high efficiency and easy implementation.

scholarly journals Mirrored Orthogonal Sampling for Covariance Matrix Adaptation Evolution Strategies

2019 ◽  
Vol 27 (4) ◽  
pp. 699-725 ◽  
Author(s):  
Hao Wang ◽  
Michael Emmerich ◽  
Thomas Bäck
Keyword(s):  
Convergence Rate ◽  
Covariance Matrix ◽  
Sampling Technique ◽  
Black Box ◽  
Evolution Strategies ◽  
High Dimensional ◽  
Major Purpose ◽  
Search Spaces ◽  
Covariance Matrix Adaptation ◽  
Adaptation Evolution

Generating more evenly distributed samples in high dimensional search spaces is the major purpose of the recently proposed mirrored sampling technique for evolution strategies. The diversity of the mutation samples is enlarged and the convergence rate is therefore improved by the mirrored sampling. Motivated by the mirrored sampling technique, this article introduces a new derandomized sampling technique called mirrored orthogonal sampling. The performance of this new technique is both theoretically analyzed and empirically studied on the sphere function. In particular, the mirrored orthogonal sampling technique is applied to the well-known Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The resulting algorithm is experimentally tested on the well-known Black-Box Optimization Benchmark (BBOB). By comparing the results from the benchmark, mirrored orthogonal sampling is found to outperform both the standard CMA-ES and its variant using mirrored sampling.

scholarly journals Adaptation, learning, Bildung: Discussion with edu- and biosemiotics

2018 ◽  
Vol 46 (4) ◽  
pp. 435-451 ◽  
Author(s):  
Eetu Pikkarainen
Keyword(s):  
Control Theory ◽  
Biological Species ◽  
Human Beings ◽  
Main Form ◽  
Perceptual Control Theory ◽  
Perceptual Control ◽  
Learning And Adaptation ◽  
Adaptation Evolution

Learning and adaptation are central problems to both edusemiotics, or semiotics of education, and biosemiotics. Bildung, as an especially human way or form of learning, and evolution as the main form of adaptation for many biologists after Darwin are often regarded as mutually exclusive concepts even though human beings are undeniably one biological species among others. In this article I will try to build a bridge between the biosemiotical, edusemiotical and Bildung-theoretical stances. Central to this discussion is biosemiotician Kalevi Kull and some of his recent publications where he considers adaptation, evolution and learning. The primary theoretical resource that I utilize here, in addition to the general Greimassian, edusemiotical and Bildung-theoretical starting points, is perceptual control theory (PCT) to which I compare the Uexküllian conception of functional circle.

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