Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Parameter optimization for visual obstacle detection using a derandomized evolution strategy
Imaging and vision systems
Evolution Strategy with Neighborhood Attraction Using a Neural Gas Approach
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Reducing Random Fluctuations in Mutative Self-adaptation
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Qualms regarding the optimality of cumulative path length control in CSA/CMA-evolution strategies
Evolutionary Computation
Automatic tuning of PID and gain scheduling PID controllers by a derandomized evolution strategy
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A New Approach for Predicting the Final Outcome of Evolution Strategy Optimization Under Noise
Genetic Programming and Evolvable Machines
Convergence results for the (1, λ)-SA-ES using the theory of ϕ-irreducible Markov chains
Theoretical Computer Science
Some comments on evolutionary algorithm theory
Evolutionary Computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Why noise may be good: additive noise on the sharp ridge
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Performance analysis of derandomized evolution strategies in quantum control experiments
Proceedings of the 10th annual conference on Genetic and evolutionary computation
On strategy parameter control by Meta-ES
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
How to Do Recombination in Evolution Strategies: An Empirical Study
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy
Adaptive niche radii and niche shapes approaches for niching with the cma-es
Evolutionary Computation
Mutative self-adaptation on the sharp and parabolic ridge
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
Multi-criteria airfoil design with evolution strategies
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
The steady state behavior of (µ/µI, λ)-ES on ellipsoidal fitness models disturbed by noise
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Optimizing data transformations for classification tasks
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Fundamenta Informaticae - Intelligent Data Analysis in Granular Computing
Surrogate constraint functions for CMA evolution strategies
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Autonomous experimental design optimization of a flapping wing
Genetic Programming and Evolvable Machines
A review of constraint-handling techniques for evolution strategies
Applied Computational Intelligence and Soft Computing - Special issue on theory and applications of evolutionary computation
An algorithm for distributed on-line, on-board evolutionary robotics
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Variance scaling for EDAs revisited
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Evolving linear transformations with a rotation-angles/scaling representation
Expert Systems with Applications: An International Journal
Searching for balance: understanding self-adaptation on ridge functions
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Power prediction in smart grids with evolutionary local kernel regression
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Evolutionary kernel density regression
Expert Systems with Applications: An International Journal
Estimating meme fitness in adaptive memetic algorithms for combinatorial problems
Evolutionary Computation
On evolutionary approaches to unsupervised nearest neighbor regression
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Applying evolution strategies to preprocessing EEG signals for brain-computer interfaces
Information Sciences: an International Journal
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Reducing the learning time of tetris in evolution strategies
EA'11 Proceedings of the 10th international conference on Artificial Evolution
A survey on optimization metaheuristics
Information Sciences: an International Journal
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Comparable to other optimization techniques, the performance of evolution strategies (ESs) depends on a suitable choice of internal strategy control parameters. Apart from a fixed setting, ESs facilitate an adjustment of such parameters within a self-adaptation process. For step-size control in particular, various adaptation concepts have been evolved early in the development of ESs. These algorithms mostly work very efficiently as long as the scaling of the parameters to be optimized is known. If the scaling is not known, the strategy has to adapt individual step-sizes for all the parameters. In general, the number of necessary step-sizes (variances) equals the dimension of the problem. In this case, step-size adaptation proves to be difficult, and the algorithms known are not satisfactory. The algorithm presented in this paper is based on the well-known concept of mutative step-size control. Our investigations indicate that the adaptation by this concept declines due to an interaction of the random elements involved. We show that this weak point of mutative step-size control can be avoided by relatively small changes in the algorithm. The modifications may be summarized by the word “derandomization.” The derandomized scheme of mutative step-size control facilitates a reliable self-adaptation of individual step-sizes.