Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Memory-based immigrants for genetic algorithms in dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A self-organizing random immigrants genetic algorithm for dynamic optimization problems
Genetic Programming and Evolvable Machines
Unsupervised fuzzy learning and cluster seeking
Intelligent Data Analysis
A Generalized Approach to Construct Benchmark Problems for Dynamic Optimization
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Genetic Algorithms with Elitism-Based Immigrants for Changing Optimization Problems
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Nearest prototype classification: clustering, genetic algorithms, or random search?
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
Population-Based Incremental Learning With Associative Memory for Dynamic Environments
IEEE Transactions on Evolutionary Computation
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Recently, genetic algorithms (GAs) have been applied to multi-modal dynamic optimization (MDO). In this kind of optimization, an algorithm is required not only to find the multiple optimal solutions but also to locate a dynamically changing optimum. Our fuzzy genetic sharing (FGS) approach is based on a novel genetic algorithm with dynamic niche sharing (GADNS). FGS finds the optimal solutions, while maintaining the diversity of the population. For this, FGS uses several strategies. First, an unsupervised fuzzy clustering method is used to track multiple optima and perform GADNS. Second, a modified tournament selection is used to control selection pressure. Third, a novel mutation with an adaptive mutation rate is used to locate unexplored search areas. The effectiveness of FGS in dynamic environments is demonstrated using the generalized dynamic benchmark generator (GDBG).