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
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming
Genetic Programming and Evolvable Machines
Neutral Networks and Evolvability with Complex Genotype-Phenotype Mapping
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
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
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Formal Algorithms + Formal Representations = Search Strategies
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Neutrality and the Evolvability of Boolean Function Landscape
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Finding Needles in Haystacks Is Not Hard with Neutrality
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Reducing Epistasis in Combinatorial Problems by Expansive Coding
Proceedings of the 5th International Conference on Genetic Algorithms
Redundant representations in evolutionary computation
Evolutionary Computation
Neutrality: a necessity for self-adaptation
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Normalization in genetic algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Representation invariant genetic operators
Evolutionary Computation
Representation and structural difficulty in genetic programming
IEEE Transactions on Evolutionary Computation
Normalization for Genetic Algorithms With Nonsynonymously Redundant Encodings
IEEE Transactions on Evolutionary Computation
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Successful and efficient use of evolutionary algorithms (EAs) depends on the choice of the genotype, the problem representation (mapping from genotype to phenotype) and on the choice of search operators that are applied to the genotypes. These choices cannot be made independently of each other. The question whether a certain representation leads to better performing EAs than an alternative representation can only be answered when the operators applied are taken into consideration. The reverse is also true: deciding between alternative operators is only meaningful for a given representation. In EA practice one can distinguish two complementary approaches. The first approach uses indirect representations where a solution is encoded in a standard data structure, such as strings, vectors, or discrete permutations, and standard off-the-shelf search operators are applied to these genotypes. To evaluate the solution, the genotype needs to be mapped to the phenotype space. The proper choice of this genotype-phenotype mapping is important for the performance of the EA search process. The second approach, the direct representation, encodes solutions to the problem in its most 'natural' space and designs search operators to operate on this representation.