An insight into bio-inspired and evolutionary algorithms for global optimization: review, analysis, and lessons learnt over a decade of competitions
Over the recent years, continuous optimization has significantly evolved to become the
mature research field it is nowadays. Through this process, evolutionary algorithms had an …
mature research field it is nowadays. Through this process, evolutionary algorithms had an …
Sgdr: Stochastic gradient descent with warm restarts
I Loshchilov, F Hutter - arXiv preprint arXiv:1608.03983, 2016 - arxiv.org
Restart techniques are common in gradient-free optimization to deal with multimodal
functions. Partial warm restarts are also gaining popularity in gradient-based optimization to …
functions. Partial warm restarts are also gaining popularity in gradient-based optimization to …
Hybrid sampling evolution strategy for solving single objective bound constrained problems
G Zhang, Y Shi - 2018 IEEE Congress on Evolutionary …, 2018 - ieeexplore.ieee.org
This paper proposes an evolution strategy (ES) algorithm called hybrid sampling-evolution
strategy (HS-ES) that combines the covariance matrix adaptation-evolution strategy (CMA …
strategy (HS-ES) that combines the covariance matrix adaptation-evolution strategy (CMA …
CMA-ES with restarts for solving CEC 2013 benchmark problems
I Loshchilov - 2013 IEEE Congress on Evolutionary …, 2013 - ieeexplore.ieee.org
This paper investigates the performance of 6 versions of Covariance Matrix Adaptation
Evolution Strategy (CMAES) with restarts on a set of 28 noiseless optimization problems …
Evolution Strategy (CMAES) with restarts on a set of 28 noiseless optimization problems …
The effect of information utilization: Introducing a novel guiding spark in the fireworks algorithm
The fireworks algorithm (FWA) is a competitive swarm intelligence algorithm which has been
shown to be very useful in many applications. In this paper, a novel guiding spark (GS) is …
shown to be very useful in many applications. In this paper, a novel guiding spark (GS) is …
Simplify your covariance matrix adaptation evolution strategy
HG Beyer, B Sendhoff - IEEE Transactions on Evolutionary …, 2017 - ieeexplore.ieee.org
The standard covariance matrix adaptation evolution strategy (CMA-ES) comprises two
evolution paths, one for the learning of the mutation strength and one for the rank-1 update …
evolution paths, one for the learning of the mutation strength and one for the rank-1 update …
Evolution strategies for continuous optimization: A survey of the state-of-the-art
Evolution strategies are a class of evolutionary algorithms for black-box optimization and
achieve state-of-the-art performance on many benchmarks and real-world applications …
achieve state-of-the-art performance on many benchmarks and real-world applications …
[图书][B] Multimodal optimization by means of evolutionary algorithms
M Preuss - 2015 - Springer
This book is the result of a very long journey into optimization, and, more specifically, into
evolutionary computation. This journey would not have been possible without the support of …
evolutionary computation. This journey would not have been possible without the support of …
Diagonal acceleration for covariance matrix adaptation evolution strategies
We introduce an acceleration for covariance matrix adaptation evolution strategies (CMA-
ES) by means of adaptive diagonal decoding (dd-CMA). This diagonal acceleration endows …
ES) by means of adaptive diagonal decoding (dd-CMA). This diagonal acceleration endows …
Enhancing the performance of differential evolution with covariance matrix self-adaptation
Differential evolution (DE) is an efficient global optimizer, while the covariance matrix
adaptation evolution strategy (CMA-ES) shows great power on local search. However …
adaptation evolution strategy (CMA-ES) shows great power on local search. However …