A Covariance Matrix Self-Adaptation Evolution Strategy for Optimization Under Linear Constraints
نویسندگان
چکیده
منابع مشابه
Task Scheduling Algorithm Using Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in Cloud Computing
The cloud computing is considered as a computational model which provides the uses requests with resources upon any demand and needs.The need for planning the scheduling of the user's jobs has emerged as an important challenge in the field of cloud computing. It is mainly due to several reasons, including ever-increasing advancements of information technology and an increase of applications and...
متن کاملMultimodal Optimization by Covariance Matrix Self-Adaptation Evolution Strategy with Repelling Subpopulations
During the recent decades, many niching methods have been proposed and empirically verified on some available test problems. They often rely on some particular assumptions associated with the distribution, shape, and size of the basins, which can seldom be made in practical optimization problems. This study utilizes several existing concepts and techniques, such as taboo points, normalized Maha...
متن کاملA modified Covariance Matrix Adaptation Evolution Strategy with adaptive penalty function and restart for constrained optimization
In the last decades, a number of novel meta-heuristics and hybrid algorithms have been proposed to solve a great variety of optimization problems. Among these, constrained optimization problems are considered of particular interest in applications from many different domains. The presence of multiple constraints can make optimization problems particularly hard to solve, thus imposing the use of...
متن کاملScaling Up Covariance Matrix Adaptation Evolution Strategy Using Cooperative Coevolution
1: procedure CC-CMA-ES(dim, subN um, lambda, ub, lb, maxF Es) 2: pop(1 : 200, 1 : dim) ← random population 3: (best, best val) ← evaluate(pop) 4: f es ← 200 5: C ← dim × dim unit matrix 6: xw ← dim × 1 random vector 7: σ ← (ub − lb) ÷ 2 8: historyW indow ← 5 9: perf ormanceRecord ← ones(3, historyW indow) 10: while f es < maxF Es do 11: (subInf o, decomposerID) ← adaptiveDecompose(dim, subN um,...
متن کاملCovariance Matrix Adaptation Revisited - The CMSA Evolution Strategy -
The covariance matrix adaptation evolution strategy (CMA-ES) rates among the most successful evolutionary algorithms for continuous parameter optimization. Nevertheless, it is plagued with some drawbacks like the complexity of the adaptation process and the reliance on a number of sophisticatedly constructed strategy parameter formulae for which no or little theoretical substantiation is availa...
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: IEEE Transactions on Evolutionary Computation
سال: 2019
ISSN: 1089-778X,1089-778X,1941-0026
DOI: 10.1109/tevc.2018.2871944