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Titlebook: Genetic and Evolutionary Computation - GECCO 2003; Genetic and Evolutio Erick Cantú-Paz,James A. Foster,Julian Miller Conference proceeding

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https://doi.org/10.1007/978-3-642-19795-6 using the non-dominated sorting concept and two parameter-free niching methods, NSPSO and its variants have shown remarkable performance against a set of well-known difficult test functions (ZDT series). Our results and comparison with NSGA II show that NSPSO is highly competitive with existing evolutionary and PSO multiobjective algorithms.
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A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization using the non-dominated sorting concept and two parameter-free niching methods, NSPSO and its variants have shown remarkable performance against a set of well-known difficult test functions (ZDT series). Our results and comparison with NSGA II show that NSPSO is highly competitive with existing evolutionary and PSO multiobjective algorithms.
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rossover was used to break two haploid of same individual into small units and remix them thoroughly. Then better phenotype was rebuilt from diploid of zygote in development phase. We introduced a new representation for Hamiltonian Cycle Problem and implemented an algorithm to test the system.
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A New Approach to Improve Particle Swarm Optimizationted in [0,1] was introduced to improve the performance of PSO algorithm in this work. Three benchmark functions were used to test the new method. The results were presented to show that the new method is effective.
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Study Diploid System by a Hamiltonian Cycle Problem Algorithmrossover was used to break two haploid of same individual into small units and remix them thoroughly. Then better phenotype was rebuilt from diploid of zygote in development phase. We introduced a new representation for Hamiltonian Cycle Problem and implemented an algorithm to test the system.
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