Evolutionary Algorithms: The Role of Mutation and Recombination
Evolutionary Algorithms: The Role of Mutation and Recombination
An Empirical Study on GAs "Without Parameters"
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Dynamically tuning the population size in particle swarm optimization
Proceedings of the 2008 ACM symposium on Applied computing
Selection for group-level efficiency leads to self-regulation of population size
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Population size reduction for the differential evolution algorithm
Applied Intelligence
Incremental Particle Swarm-Guided Local Search for Continuous Optimization
HM '08 Proceedings of the 5th International Workshop on Hybrid Metaheuristics
The Role of Population Size in Rate of Evolution in Genetic Programming
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Improving genetic algorithms performance via deterministic population shrinkage
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Genetic Programming and Evolvable Machines
Information Sciences: an International Journal
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In this paper we analyze a new method for an adaptive variation of Evolutionary Algorithms (EAs) population size: the Self-Regulated Population size EA (SRP-EA). An empirical evaluation of the method is provided by comparing the new proposal with the CHC algorithm and other well known EAs with varying population. A fitness landscape generator was chosen to test and compare the algorithms: the Spear's multimodal function generator. The performance of the algorithms was measured in terms of success rate, quality of the solutions and evaluations needed to attain them over a wide range of problem instances. We will show that SRP-EA performs well on these tests and appears to overcome some recurrent drawbacks of traditional EAs which lead them to local optima premature convergence. Also, unlike other methods, SRP-EA seems to self-regulate its population size according to the state of the search.