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
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
The Ant Colony Metaphor for Searching Continuous Design Spaces
Selected Papers from AISB Workshop on Evolutionary Computing
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Free search: a comparative analysis
Information Sciences—Informatics and Computer Science: An International Journal
Novel Adaptive Heuristic For Search And Optimisation
JVA '06 Proceedings of the IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing
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The article presents an adaptive method, called Free Search. It implements ideas different from other evolutionary algorithms such as Genetic Algorithms, Particle Swarm Optimisation, Differential Evolution and Ant Colony Optimisation. Free Search is based on original concepts for individual intelligence and independence of the population members. It is applied to optimisation of time dependent data and tries to find and to track optimal solutions, which change their locations during the period of search. The aim is to find an answer to the question - how Free Search behaves when the global optimum leaves the search space. The achieved experimental results are presented and discussed.