Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
On evolutionary exploration and exploitation
Fundamenta Informaticae
Finite Markov chain results in evolutionary computation: a tour d'horizon
Fundamenta Informaticae
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Zen and the Art of Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Generalized Convergence Models for Tournament- and (mu, lambda)-Selection
Proceedings of the 6th International Conference on Genetic Algorithms
On the Desired Behaviors of Self-Adaptive Evolutionary Algorithms
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Resource-Based Fitness Sharing
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Toward a theory of evolution strategies: On the benefits of sex---the (μ/μ, λ) theory
Evolutionary Computation
A comparison of selection schemes used in evolutionary algorithms
Evolutionary Computation
On the futility of blind search: An algorithmic view of “no free lunch”
Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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Evolutionary algorithms are often successfully applied to hard optimization problems. However, besides rules of thumb and experience, trial and error is still the leading design technique for evolutionary algorithms. A profound theoretical foundation guiding those applications is still missing. This article outlines a networked understanding of evolutionary algorithms. As a first step towards that goal, it reviews and interrelates major theoretical results and working principles in order to give an extensive insight into the internal processing of evolutionary algorithms. This not only helps to understand success and failure of evolutionary algorithms in applications but, in addition, could lead to a theory-guided design process enrichening and relieving today's heuristic techniques.