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
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Optimizing of NC tool paths for five-axis milling using evolutionary algorithms on wavelets
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
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ICSPEA is a novel multi-objective evolutionary algorithm which integrates aspects from the powerful variation operators of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and the well proven multi-objective Strength Pareto Evaluation Scheme of the SPEA 2. The CMA-ES has already shown excellent performance on various kinds of complex single-objective problems. The evaluation scheme of the SPEA 2 selects individuals with respect to their current position in the objective space using a scalar index in order to form proper Pareto front approximations. These indices can be used by the CMA-part of ICSPEA for learning and guiding the search towards better Pareto front approximations. The ICSPEA is applied to complex benchmark functions such as an extended n-dimensional Schaffer's function or Quagliarella's problem. The results show that the CMA operator allows ICSPEA to find the Pareto set of the generalised Schaffer's function faster than SPEA 2. Furthermore, this concept is tested on the complex real-world application of the multi-objective optimization of five-axis milling NC-paths. An application of ICSPEA to the milling-path optimisation problem yielded efficient sets of five-axis NC-paths.