Parallel Evolutionary Algorithms and High Dimensional Optimization Problems
نویسندگان
چکیده
منابع مشابه
Parallel Global Optimization of High-Dimensional Problems
A parallel version of an optimization algorithm for arbitrary functions of arbitrary dimension N has been developed and tested on an IBM-Regatta HPC system equipped with 16 CPUs of Power4 type, each with 1.3 GHz clock frequency. The optimization algorithm follows a simplex-like stochastic search technique aimed at quasi-complete sampling of all the local minima. Parallel functionality is incorp...
متن کاملParallel Optimization of Evolutionary Algorithms
A parallel two-level evolutionary algorithm which evolves genetic algorithms of maximum convergence velocity is presented. The meta-algorithm combines principles of evolution strategies and genetic algorithms in order to optimize continuous and discrete parameters of the genetic algorithms at the same time (mixed-integer optimization). The genetic algorithms which result from the meta-evolution...
متن کاملParallel evolutionary algorithm in high-dimensional optimization problem
An implementation of the combined evolutionary algorithm for searching extremum of functions with many parameters is proposed. The algorithm designed to optimize parameters of the molecular-dynamics reactive force field potential ReaxFF also can be efficient in many other extrema-searching problems with arbitrary complex objective function. The algorithm itself is a hybrid of two evolutionary m...
متن کاملImproving Evolutionary Algorithms with Scouting: High-Dimensional Problems
Evolutionary Algorithms (EAs) are common optimization techniques based on the concept of Darwinian evolution. During the search for the global optimum of a search space, a traditional EA will often become trapped in a local optimum. The Scouting-Inspired Evolutionary Algorithms (SEAs) are a recently–introduced family of EAs that use a cross–generational memory mechanism to overcome this problem...
متن کاملEvolutionary algorithms and dynamic optimization problems
2003 2 3 Summary This thesis examines evolutionary algorithms, a universal optimization method, applied to dynamic problems, i.e. the problems are changing during optimization. The thesis is motivated by a lack of foundations for the field and the incomparability of most publications that are of an empirical nature. To establish a basis for the comparison of evolutionary algorithms applied to d...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computers
سال: 2018
ISSN: 1796-203X
DOI: 10.17706/jcp.13.11.1265-1271