Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Journal of Global Optimization
Advances in evolutionary computing
A Note on the Extended Rosenbrock Function
Evolutionary Computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Population size reduction for the differential evolution algorithm
Applied Intelligence
Adaptation in differential evolution: A numerical comparison
Applied Soft Computing
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
JADE: adaptive differential evolution with optional external archive
IEEE Transactions on Evolutionary Computation
Differential evolution in constrained numerical optimization: An empirical study
Information Sciences: an International Journal
A clustering-based differential evolution for global optimization
Applied Soft Computing
Multi-objective Genetic Algorithms for grouping problems
Applied Intelligence
Population-based algorithm portfolios for numerical optimization
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
PC2PSO: personalized e-course composition based on Particle Swarm Optimization
Applied Intelligence
Study on hybrid PS-ACO algorithm
Applied Intelligence
Psychological model of particle swarm optimization based multiple emotions
Applied Intelligence
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Evolutionary programming using mutations based on the Levy probability distribution
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Enhanced Differential Evolution With Adaptive Strategies for Numerical Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A two-leveled symbiotic evolutionary algorithm for clustering problems
Applied Intelligence
Recurring Two-Stage Evolutionary Programming: A Novel Approach for Numeric Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Performance evaluation of evolutionary heuristics in dynamic environments
Applied Intelligence
LADPSO: using fuzzy logic to conduct PSO algorithm
Applied Intelligence
Dynamic clustering using combinatorial particle swarm optimization
Applied Intelligence
Adaptive cooperative particle swarm optimizer
Applied Intelligence
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This paper describes a dynamic group-based differential evolution (GDE) algorithm for global optimization problems. The GDE algorithm provides a generalized evolution process based on two mutation operations to enhance search capability. Initially, all individuals in the population are grouped into a superior group and an inferior group based on their fitness values. The two groups perform different mutation operations. The local mutation model is applied to individuals with better fitness values, i.e., in the superior group, to search for better solutions near the current best position. The global mutation model is applied to the inferior group, which is composed of individuals with lower fitness values, to search for potential solutions. Subsequently, the GDE algorithm employs crossover and selection operations to produce offspring for the next generation. In this paper, an adaptive tuning strategy based on the well-known 1/5th rule is used to dynamically reassign the group size. It is thus helpful to trade off between the exploration ability and the exploitation ability. To validate the performance of the GDE algorithm, 13 numerical benchmark functions are tested. The simulation results indicate that the approach is effective and efficient.