A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Global optimization
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Building Better Test Functions
Proceedings of the 6th International Conference on Genetic Algorithms
Genetic Algorithms and Evolution Strategies - Similarities and Differences
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Parameter Control within a Co-operative Co-evolutionary Genetic Algorithm
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Adaption of Operator Probabilities in Genetic Programming
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
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
Applying the triple parameter hypothesis to maintenance scheduling
Proceedings of the 12th annual conference on Genetic and evolutionary computation
An exploration into dynamic population sizing
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Is the triple parameter hypothesis generalizable
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Evolvability and speed of evolutionary algorithms in light of recent developments in biology
Journal of Artificial Evolution and Applications
A novel evolutionary clustering algorithm based on Gaussian mixture model
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
A parameter-less genetic algorithm with customized crossover and mutation operators
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Idealized dynamic population sizing for uniformly scaled problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Is self-adaptation of selection pressure and population size possible?: a case study
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Self-regulated population size in evolutionary algorithms
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Evolutionary computing and autonomic computing: shared problems, shared solutions?
Self-star Properties in Complex Information Systems
The sandpile mutation operator for genetic algorithms
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
A note on teaching-learning-based optimization algorithm
Information Sciences: an International Journal
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Entropy-based adaptive range parameter control for evolutionary algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
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In this paper we implement GAs that have one or more parameters that are adjusted during the run. In particular we use an existing self-adaptive mutation rate mechanism, propose a new mechanism for self-adaptive crossover rates, and redesign an existing variable population size model. We compare the simple GA with GAs featuring only one of the parameter adjusting mechanisms and with a GA that applies all three mechanisms - and is therefore almost "parameterless". The experimental results on a carefully designed test suite indicate the superiority of the parameterless GA and give a hint on the power of adapting the population size.