Self-adaptation in evolving systems
Artificial Life
Swarm intelligence
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Journal of Global Optimization
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
A population-based algorithm-generator for real-parameter optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Convergence in Evolutionary Programs with Self-Adaptation
Evolutionary Computation
A Comparison Study of Self-Adaptation in Evolution Strategies and Real-Coded Genetic Algorithms
Evolutionary Computation
A comparative study of stochastic optimization methods in electric motor design
Applied Intelligence
Computational Optimization and Applications
Parameter-less evolutionary search
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Adaptive strategy selection in differential evolution
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Thermal Simulation for Development Speed-Up
SIMUL '10 Proceedings of the 2010 Second International Conference on Advances in System Simulation
Guided restarting local search for production planning
Engineering Applications of Artificial Intelligence
Self-adaptive differential evolution with multi-trajectory search for large-scale optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
The differential ant-stigmergy algorithm
Information Sciences: an International Journal
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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The development of a simple, adaptive, parameter-less search algorithm was initiated by the need for an algorithm that is able to find optimal solutions relatively quick, and without the need for a control-parameter-setting specialist. Its control parameters are calculated during the optimization process, according to the progress of the search. The algorithm is intended for continuous and combinatorial problems. The efficiency of the proposed parameter-less algorithm was evaluated using one theoretical and three real-world industrial optimization problems. A comparison with other evolutionary approaches shows that the presented adaptive parameter-less algorithm has a competitive convergence with regards to the comparable algorithms. Also, it proves algorithm's ability to finding the optimal solutions without the need for predefined control parameters.