Using Covariance Matrix Adaptation Evolutionary Strategy to boost the search accuracy in hierarchic memetic computations

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

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 کامل

Contextual Covariance Matrix Adaptation Evolutionary Strategies

Many stochastic search algorithms are designed to optimize a fixed objective function to learn a task, i.e., if the objective function changes slightly, for example, due to a change in the situation or context of the task, relearning is required to adapt to the new context. For instance, if we want to learn a kicking movement for a soccer robot, we have to relearn the movement for different bal...

متن کامل

Scaling 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 کامل

Covariance 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 کامل

A Covariance Matrix Adaptation Evolution Strategy for Direct Policy Search in Reproducing Kernel Hilbert Space

The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient derivativefree optimization algorithm. It optimizes a black-box objective function over a well defined parameter space. In some problems, such parameter spaces are defined using function approximation in which feature functions are manually defined. Therefore, the performance of those techniques strongly depends on the...

متن کامل

ذخیره 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 Computational Science

سال: 2019

ISSN: 1877-7503

DOI: 10.1016/j.jocs.2019.04.005