Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Evolutionary Algorithms: The Role of Mutation and Recombination
Evolutionary Algorithms: The Role of Mutation and Recombination
Adaptively Resizing Populations: An Algorithm and Analysis
Proceedings of the 5th International Conference on Genetic Algorithms
An Empirical Study on GAs "Without Parameters"
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Self-Adaptive Genetic Algorithm for Numeric Functions
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Intelligent Mutation Rate Control in Canonical Genetic Algorithms
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
A self-adaptive evolutionary negative selection approach for anomaly detection
A self-adaptive evolutionary negative selection approach for anomaly detection
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
An exploration into dynamic population sizing
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Adaptive genetic algorithm using harmony search
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Meta-evolved empirical evidence of the effectiveness of dynamic parameters
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Self-adaptation of parameters in a learning classifier system ensemble machine
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
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In this paper we seek an answer to the following question: Is it possible and rewarding to self-adapt parameters regarding selection and population size in an evolutionary algorithm? The motivation comes from the observation that the majority of the existing EC literature is concerned with (self-)adaptation of variation operators, while there are indications that (self-)adapting selection operators or the population size can be equally or even more rewarding. We approach the question in an empirical manner. We design and execute experiments for comparing the performance increase of a benchmark EA when augmented with self-adaptive control of parameters concerning selection and population size in isolation and in combination. With the necessary caveats regarding the test suite and the particular mechanisms used we observe that self-adapting selection yields the highest benefit (up to 30-40%) in terms of speed.