Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Combining Evolutionary, Connectionist, and Fuzzy Classification Algorithms for Shape Analysis
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
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
Breeding swarms: a GA/PSO hybrid
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Opposition-Based Learning: A New Scheme for Machine Intelligence
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Circle detection on images using genetic algorithms
Pattern Recognition Letters
A novel population initialization method for accelerating evolutionary algorithms
Computers & Mathematics with Applications
An Improved Chaotic Particle Swarm Optimization and Its Application in Investment
ISCID '08 Proceedings of the 2008 International Symposium on Computational Intelligence and Design - Volume 01
An evolutionary algorithm with species-specific explosion for multimodal optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Chaotic hybrid algorithm and its application in circle detection
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
IEEE Transactions on Evolutionary Computation
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Survey A review of opposition-based learning from 2005 to 2012
Engineering Applications of Artificial Intelligence
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An evolutionary circle detection method based on a novel Chaotic Hybrid Algorithm (CHA) is proposed. The method combines the strengths of particle swarm optimization, genetic algorithms and chaotic dynamics, and involves the standard velocity and position update rules of PSOs, with the ideas of selection, crossover and mutation from GA. The opposition-based learning (OBL) is employed in CHA for population initialization. In addition, the notion of species is introduced into the proposed CHA to enhance its performance in solving multimodal problems. The effectiveness of the Species-based Chaotic Hybrid Algorithm (SCHA) is proven through simulations and benchmarking; finally it is successfully applied to solve circle detection problems. To make it more powerful in solving circle detection problems in complicated circumstances, the notion of 'tolerant radius' is proposed and incorporated into the SCHA-based method. Simulation tests were undertaken on several hand drawn sketches and natural photos, and the effectiveness of the proposed method was clearly shown in the test results.