Proceedings of the fourth international conference on Genetic algorithms
Proceedings of the fourth international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Evolving cellular automata to perform computations: mechanisms and impediments
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
An introduction to genetic algorithms
An introduction to genetic algorithms
Statistical dynamics of the Royal Road genetic algorithm
Theoretical Computer Science - Special issue on evolutionary computation
Practical Handbook of Genetic Algorithms
Practical Handbook of Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Proceedings of the 5th International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
When Will a Genetic Algorithm Outperform Hill Climbing?
Proceedings of the 5th International Conference on Genetic Algorithms
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Multimodal Performance Profiles on the Adaptive Distributed Database Management Problem
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
The Cooperative Coevolutionary (1+1) EA
Evolutionary Computation
Deceptiveness and neutrality the ND family of fitness landscapes
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Optimum tracking with evolution strategies
Evolutionary Computation
On Replacement Strategies in Steady State Evolutionary Algorithms
Evolutionary Computation
Exploring the explorative advantage of the cooperative coevolutionary (1+1) EA
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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Epochal dynamics, in which long periods of stasis in an evolving population are punctuated by a sudden burst of change, is a common behavior in both natural and artificial evolutionary processes. We analyze the population dynamics for a class of fitness functions that exhibit epochal behavior using a mathematical framework developed recently, which incorporates techniques from the fields of mathematical population genetics, molecular evolution theory, and statistical mechanics. Our analysis predicts the total number of fitness function evaluations to reach the global optimum as a function of mutation rate, population size, and the parameters specifying the fitness function. This allows us to determine the optimal evolutionary parameter settings for this class of fitness functions.We identify a generalized error threshold that smoothly bounds the two-dimensional regime of mutation rates and population sizes for which epochal evolutionary search operates most efficiently. Specifically, we analyze the dynamics of epoch destabilization under finite-population sampling fluctuations and show how the evolutionary parameters effectively introduce a coarse graining of the fitness function. More generally, we find that the optimal parameter settings for epochal evolutionary search correspond to behavioral regimes in which the consecutive epochs are marginally stable against the sampling fluctuations. Our results suggest that in order to achieve optimal search, one should set evolutionary parameters such that the coarse graining of the fitness function induced by the sampling fluctuations is just large enough to hide local optima.