A self-adaptive multiagent evolutionary algorithm for electrical machine design
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Peer-to-peer evolutionary algorithms with adaptive autonomous selection
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Dynamically tuning the population size in particle swarm optimization
Proceedings of the 2008 ACM symposium on Applied computing
DFS Based Partial Pathways in GA for Protein Structure Prediction
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
A novel and accelerated genetic algorithm
WSEAS Transactions on Systems and Control
Improving genetic algorithms performance via deterministic population shrinkage
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Genetic Programming and Evolvable Machines
Quantum-inspired evolutionary algorithms: a survey and empirical study
Journal of Heuristics
Fundamental matrix estimation by multiobjective genetic algorithm with Taguchi's method
Applied Soft Computing
Self-adaptive randomized and rank-based differential evolution for multimodal problems
Journal of Global Optimization
Self-regulated population size in evolutionary algorithms
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Step length adaptation by generalized predictive control
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Adaptive multi-objective genetic algorithm using multi-pareto-ranking
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Discovering the rules of a elementary one-dimensional automaton
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Registrar: a complete-memory operator to enhance performance of genetic algorithms
Journal of Global Optimization
International Journal of Applied Evolutionary Computation
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
Transactions on Computational Collective Intelligence IX
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A genetic algorithm (GA) is proposed that uses a variable population size and periodic partial reinitialization of the population in the form of a saw-tooth function. The aim is to enhance the overall performance of the algorithm relying on the dynamics of evolution of the GA and the synergy of the combined effects of population size variation and reinitialization. Preliminary parametric studies to test the validity of these assertions are performed for two categories of problems, a multimodal function and a unimodal function with different features. The proposed scheme is compared with the conventional GA and micro GA (μGA) of equal computing cost and guidelines for the selection of effective values of the involved parameters are given, which facilitate the implementation of the algorithm. The proposed algorithm is tested for a variety of benchmark problems and a problem generator from which it becomes evident that the saw-tooth scheme enhances the overall performance of GAs.