Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
New ideas in optimization
The ant colony optimization meta-heuristic
New ideas in optimization
An introduction to differential evolution
New ideas in optimization
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Evolutionary Computation at the Edge of Feasibility
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The Ant Colony Metaphor for Searching Continuous Design Spaces
Selected Papers from AISB Workshop on Evolutionary Computing
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Human evolutionary model: A new approach to optimization
Information Sciences: an International Journal
A self-adaptive migration model genetic algorithm for data mining applications
Information Sciences: an International Journal
A general framework for statistical performance comparison of evolutionary computation algorithms
Information Sciences: an International Journal
Free search in tracking time dependent optima
EC'08 Proceedings of the 9th WSEAS International Conference on Evolutionary Computing
Self-organizing genetic algorithm based tuning of PID controllers
Information Sciences: an International Journal
Free search differential evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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The article presents a novel population-based optimisation method, called Free Search (FS). Essential peculiarities of the new method are introduced. The aim of the study is to identify how robust is Free Search. Explored and compared are four different population-based optimisation methods, namely Genetic Algorithm (in real coded BLX-α modification), Particle Swarm Optimisation, Differential Evolution and Free Search. They are applied to five non-linear, heterogeneous, numerical, optimisation problems. The achieved results suggest that Free Search has stable robust behaviour on explored tests; FS can cope with heterogeneous optimisation problems; FS is applicable to unknown (black-box) real-world optimisation tasks.