Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition)
Integrating user preferences with particle swarms for multi-objective optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Choosing Leaders for Multi-objective PSO Algorithms Using Differential Evolution
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Design Optimization of Radio Frequency Discrete Tuning Varactors
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Empirical comparison of MOPSO methods: guide selection and diversity preservation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A PSO-based topology control algorithm in wireless sensor networks
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Multi-objective maximin sorting scheme
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
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The maximin fitness function can be used in multi-objective genetic algorithms to obtain a diverse set of non-dominated designs. The maximin fitness function is derived from the definition of dominance, and its properties are explored. The modified maximin fitness function is proposed. Both fitness functions are briefly compared to a state-of-the-art fitness function from the literature. Results from a real-world multi-objective problem are presented. This problem addresses land-use and transportation planning for high-growth cities and metropolitan regions.