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Hybrid Evolutionary Search Method Based on Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Programmable self-assembly using biologically-inspired multiagent control
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Schemata, Distributions and Graphical Models in Evolutionary Optimization
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Computer Networks: The International Journal of Computer and Telecommunications Networking
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Acta Cybernetica
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GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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Pattern Recognition Letters
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Proceedings of the 8th annual conference on Genetic and evolutionary computation
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Crossover: the divine afflatus in search
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Variable discrimination of crossover versus mutation using parameterized modular structure
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms
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A destructive evolutionary process: a pilot implementation
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Evolutionary Computation
Crossover accelerates evolution in gas with a babel-like fitness landscape: Mathematical analyses
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On the application of linear transformations for genetic algorithms optimization
International Journal of Knowledge-based and Intelligent Engineering Systems
Deriving evaluation metrics for applicability of genetic algorithms to optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Clustering with an N-dimensional extension of Gielis superformula
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
Problem difficulty analysis for particle swarm optimization: deception and modality
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WSEAS Transactions on Computers
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EC'09 Proceedings of the 10th WSEAS international conference on evolutionary computing
Difficulty of linkage learning in estimation of distribution algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Connectionist theory refinement: genetically searching the space of network topologies
Journal of Artificial Intelligence Research
Biologically-inspired self-assembly of two-dimensional shapes using global-to-local compilation
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Quantifying ruggedness of continuous landscapes using entropy
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An analysis of a reordering operator with tournament selection on a GA-hard problem
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
Finding irregularly shaped clusters based on entropy
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
On the performance of fitness uniform selection for non-deceptive problems
Proceedings of the 48th Annual Southeast Regional Conference
A methodology to find clusters in the data based on Shannon's entropy and genetic algorithms
ACELAE'11 Proceedings of the 10th WSEAS international conference on communications, electrical & computer engineering, and 9th WSEAS international conference on Applied electromagnetics, wireless and optical communications
Hierarchical allelic pairwise independent functions
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Negative slope coefficient: a measure to characterize genetic programming fitness landscapes
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
The deceptive degree of the objective function
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
The Gestalt heuristic: emerging abstraction to improve combinatorial search
Natural Computing: an international journal
A survey of techniques for characterising fitness landscapes and some possible ways forward
Information Sciences: an International Journal
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What makes a problem easy or hard for a genetic algorithm (GA)? This question has become increasingly important as people have tried to apply the GA to ever more diverse types of problems. Much previous work on this question has studied the relationship between GA performance and the structure of a given fitness function when it is expressed as a Walsh polynomial. The work of Bethke, Goldberg, and others has produced certain theoretical results about this relationship. In this article we review these theoretical results, and then discuss a number of seemingly anomalous experimental results reported by Tanese concerning the performance of the GA on a subclass of Walsh polynomials, some members of which were expected to be easy for the GA to optimize. Tanese found that the GA was poor at optimizing all functions in this subclass, that a partitioning of a single large population into a number of smaller independent populations seemed to improve performance, and that hillclimbing outperformed both the original and partitioned forms of the GA on these functions. These results seemed to contradict several commonly held expectations about GAs.We begin by reviewing schema processing in GAs. We then give an informal description of how Walsh analysis and Bethke's Walsh-schema transform relate to GA performance, and we discuss the relevance of this analysis for GA applications in optimization and machine learning. We then describe Tanese's surprising results, examine them experimentally and theoretically, and propose and evaluate some explanations. These explanations lead to a more fundamental question about GAs: what are the features of problems that determine the likelihood of successful GA performance?