Coding and information theory (2nd ed.)
Coding and information theory (2nd ed.)
A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Adapting operator probabilities in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
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
An approach to a problem in network design using genetic algorithms
An approach to a problem in network design using genetic algorithms
Ruggedness and neutrality—the NKp family of fitness landscapes
ALIFE Proceedings of the sixth international conference on Artificial life
A weighted coding in a genetic algorithm for the degree-constrained minimum spanning tree problem
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Neutrality in fitness landscapes
Applied Mathematics and Computation
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
The Use of Neutral Genotype-Phenotype Mappings for Improved Evolutionary Search
BT Technology Journal
Comparison of Algorithms for the Degree Constrained Minimum Spanning Tree
Journal of Heuristics
Network random keys: a tree representation scheme for genetic and evolutionary algorithms
Evolutionary Computation
The Link and Node Biased Encoding Revisited: Bias and Adjustment of Parameters
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Through the Labyrinth Evolution Finds a Way: A Silicon Ridge
ICES '96 Proceedings of the First International Conference on Evolvable Systems: From Biology to Hardware
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
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On the Utility of Redundant Encodings in Mutation-Based Evolutionary Search
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Redundant Coding of an NP-Complete Problem Allows Effective Genetic Algorithm Search
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Genetic Programming and Evolvable Machines
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Applied Intelligence
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FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
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EuroGP'07 Proceedings of the 10th European conference on Genetic programming
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Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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Genetic Programming and Evolvable Machines
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Compact genetic codes as a search strategy of evolutionary processes
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
Quotient geometric crossovers and redundant encodings
Theoretical Computer Science
Representations for evolutionary algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Genetic Programming and Evolvable Machines
Natural Computing: an international journal
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This paper discusses how the use of redundant representations influences the performance of genetic and evolutionary algorithms. Representations are redundant if the number of genotypes exceeds the number of phenotypes. A distinction is made between synonymously and non-synonymously redundant representations. Representations are synonymously redundant if the genotypes that represent the same phenotype are very similar to each other. Non-synonymously redundant representations do not allow genetic operators to work properly and result in a lower performance of evolutionary search. When using synonymously redundant representations, the performance of selectorecombinative genetic algorithms (GAs) depends on the modification of the initial supply. We have developed theoretical models for synonymously redundant representations that show the necessary population size to solve a problem and the number of generations goes with O(2kr/r), where kr is the order of redundancy and r is the number of genotypic building blocks (BB) that represent the optimal phenotypic BB. As a result, uniformly redundant representations do not change the behavior of GAs. Only by increasing r, which means overrepresenting the optimal solution, does GA performance increase. Therefore, non-uniformly redundant representations can only be used advantageously if a-priori information exists regarding the optimal solution. The validity of the proposed theoretical concepts is illustrated for the binary trivial voting mapping and the real-valued link-biased encoding. Our empirical investigations show that the developed population sizing and time to convergence models allow an accurate prediction of the empirical results.