Proceedings of the third international conference on Genetic algorithms
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Solving Cutting Stock Problems by Evolutionary Programming
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Cooperative Model for Genetic Operators to Improve GAs
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
IEEE Transactions on Evolutionary Computation
Analysis of estimation of distribution algorithms and genetic algorithms on NK landscapes
Proceedings of the 10th annual conference on Genetic and evolutionary computation
NK Landscapes Difficulty and Negative Slope Coefficient: How Sampling Influences the Results
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
An Analysis of Recombination in Some Simple Landscapes
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Web-page color modification for barrier-free color vision with genetic algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
NK landscapes, problem difficulty, and hybrid evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Sexual recombination in self-organizing interaction networks
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
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Empirical studies have shown that the overall performance of random bit climbers on NK-Landscapes is superior to the performance of some simple and enhanced GAs. Analytical studies have also lead to suggest that NK-Landscapes may not be appropriate for testing the performance of GAs. In this work we study the effect of selection, drift, mutation, and recombination on NK-Landscapes for N = 96. We take a model of generational parallel varying mutation GA (GASRM) and switch on and off its major components to emphasize each of the four processes mentioned above. We observe that using an appropriate selection pressure and postponing drift make GAs quite robust on NK-Landscapes; different to previous studies, even simple GAs with these two features perform better than a random bit climber (RBC+) for a broad range of classes of problems (K ≥ 4). We also observe that the interaction of parallel varying mutation with crossover improves further the reliability of the GA, especially for 12 K K a mutation only EA is very effective and crossover may be omitted; but the relative importance of crossover interacting with varying mutation increases with K performing better than mutation alone (K 12).We conclude that NK-Landscapes are useful for testing the GA's overall behavior and performance and also for testing each one of the major processes involved in a GA.