Evolving artificial intelligence
Evolving artificial intelligence
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Contemporary Evolution Strategies
Proceedings of the Third European Conference on Advances in Artificial Life
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Step-Size Adaption Based on Non-Local Use of Selection Information
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Toward a theory of evolution strategies: Some asymptotical results from the (1,+ λ)-theory
Evolutionary Computation
Toward a theory of evolution strategies: The (μ, λ)-theory
Evolutionary Computation
Toward a theory of evolution strategies: On the benefits of sex---the (μ/μ, λ) theory
Evolutionary Computation
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Adapting Self-Adaptive Parameters in Evolutionary Algorithms
Applied Intelligence
A Comparison of Evolution Strategies with Other Direct Search Methods in the Presence of Noise
Computational Optimization and Applications
How to analyse evolutionary algorithms
Theoretical Computer Science - Natural computing
A Pursuit Architecture for Signal Analysis
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Statistical Characteristics of Evolution Strategies
PPSN VI Proceedings of the 6th 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
Dynamic Control of Adaptive Parameters in Evolutionary Programming
SEAL'98 Selected papers from the Second Asia-Pacific Conference on Simulated Evolution and Learning on Simulated Evolution and Learning
Evolution Strategy in Portfolio Optimization
Selected Papers from the 5th European Conference on Artificial Evolution
Qualms regarding the optimality of cumulative path length control in CSA/CMA-evolution strategies
Evolutionary Computation
Approximations with evolutionary pursuit
Signal Processing
Evolutionary algorithms in modeling and animation
Handbook of computer animation
Theoretical Computer Science
Information Sciences: an International Journal - Special issue: Evolutionary computation
Automatic tuning of PID and gain scheduling PID controllers by a derandomized evolution strategy
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Genetic Programming and Evolvable Machines
Combining competent crossover and mutation operators: a probabilistic model building approach
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Convergence results for the (1, λ)-SA-ES using the theory of ϕ-irreducible Markov chains
Theoretical Computer Science
Introduction to the Special Issue: Self-Adaptation
Evolutionary Computation
Theoretical Analysis of Mutation-Adaptive Evolutionary Algorithms
Evolutionary Computation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Self-Adaptive Genetic Algorithms with Simulated Binary Crossover
Evolutionary Computation
A Convergence Analysis of Unconstrained and Bound Constrained Evolutionary Pattern Search
Evolutionary Computation
Analysis of the (μ/μ, λ) - ES on the Parabolic Ridge
Evolutionary Computation
Hierarchically organised evolution strategies on the parabolic ridge
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Weighted multirecombination evolution strategies
Theoretical Computer Science - Foundations of genetic algorithms
An analysis of mutative σ-self-adaptation on linear fitness functions
Evolutionary Computation
An evolutionary machine learning: An adaptability perspective at fine granularity
International Journal of Knowledge-based and Intelligent Engineering Systems
Some comments on evolutionary algorithm theory
Evolutionary Computation
Rigorous hitting times for binary mutations
Evolutionary Computation
Why noise may be good: additive noise on the sharp ridge
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Step length adaptation on ridge functions
Evolutionary Computation
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Evolution strategies with cumulative step length adaptation on the noisy parabolic ridge
Natural Computing: an international journal
Adaptive computational chemotaxis in bacterial foraging optimization: an analysis
IEEE Transactions on Evolutionary Computation
Mutative self-adaptation on the sharp and parabolic ridge
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
Performance of the (µ/µ, λ)-σSA-ES on a class of PDQFs
IEEE Transactions on Evolutionary Computation
An Overview of Parameter Control Methods by Self-Adaptation in Evolutionary Algorithms
Fundamenta Informaticae
Finite Markov Chain Results in Evolutionary Computation: A Tour d'Horizon
Fundamenta Informaticae
On the behaviour of the (1,λ)-σSA-ES for a constrained linear problem
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Using Evolution Strategies to Perform Stellar Population Synthesis for Galaxy Spectra from SDSS
International Journal of Applied Evolutionary Computation
Improving differential evolution through a unified approach
Journal of Global Optimization
Multi-objective evolutionary design of robust controllers on the grid
Engineering Applications of Artificial Intelligence
Computational Optimization and Applications
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This paper analyzes the self-adaptation (SA) algorithm widely used to adapt strategy parameters of the evolution strategy (ES) in order to obtain maximal ES performance. The investigations are concentrated on the adaptation of one general mutation strength σ (called σSA) in (1, λ) ESs. The hypersphere serves as the fitness model. Starting from an introduction to the basic concept of self-adaptation, a framework for the analysis of σSA is developed on two levels: a microscopic level, concerning the description of the stochastic changes from one generation to the next, and a macroscopic level, describing the evolutionary dynamics of the σSA over time (generations). The σSA requires the fixing of a new strategy parameter, known as the learning parameter. The influence of this parameter on ES performance is investigated and rules for its tuning are presented and discussed. The results of the theoretical analysis are compared with ES experiments; it will be shown that applying Schwefel's τ-scaling rule guarantees the linear convergence order of the ES.