Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Adaptive operator probabilities in a genetic algorithm that applies three operators
SAC '97 Proceedings of the 1997 ACM symposium on Applied computing
Evolution of Appropriate Crossover and Mutation Operators in a Genetic Process
Applied Intelligence
Competitive Evolution: A Natural Approach to Operator Selection
AI '93/AI '94 Selected papers from the AI'93 and AI'94 Workshops on Evolutionary Computation, Process in Evolutionary Computation
Cost Based Operator Rate Adaption: An Investigation
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Variable period adaptive genetic algorithm
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Hybrid crossover operators for real-coded genetic algorithms: an experimental study
Soft Computing - A Fusion of Foundations, Methodologies and Applications
An adaptive pursuit strategy for allocating operator probabilities
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Credit assignment in adaptive evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Adaptive genetic operators based on coevolution with fuzzybehaviors
IEEE Transactions on Evolutionary Computation
Credit assignment in adaptive evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Adaptive operator selection with dynamic multi-armed bandits
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Extreme Value Based Adaptive Operator Selection
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
A Compass to Guide Genetic Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Analysis of adaptive operator selection techniques on the royal road and long k-path problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Extreme: dynamic multi-armed bandits for adaptive operator selection
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Extreme compass and dynamic multi-armed bandits for adaptive operator selection
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Toward comparison-based adaptive operator selection
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Autonomous operator management for evolutionary algorithms
Journal of Heuristics
Analyzing bandit-based adaptive operator selection mechanisms
Annals of Mathematics and Artificial Intelligence
Adaptive strategy selection in differential evolution for numerical optimization: An empirical study
Information Sciences: an International Journal
Multi-Objective differential evolution with adaptive control of parameters and operators
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
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In this paper, the issue of adapting probabilities for Evolutionary Algorithm (EA) search operators is revisited. A framework is devised for distinguishing between measurements of performance and the interpretation of those measurements for purposes of adaptation. Several examples of measurements and statistical interpretations are provided. Probability value adaptation is tested using an EA with 10 search operators against 10 test problems with results indicating that both the type of measurement and its statistical interpretation play significant roles in EA performance. We also find that selecting operators based on the prevalence of outliers rather than on average performance is able to provide considerable improvements to adaptive methods and soundly outperforms the non-adaptive case.