Coding and information theory (2nd ed.)
Coding and information theory (2nd ed.)
A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Sizing populations for serial and parallel genetic algorithms
Proceedings of the third international conference on Genetic algorithms
An approach to a problem in network design using genetic algorithms
An approach to a problem in network design using genetic algorithms
OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems
OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Genetic Algorithm for Survivable Network Design
Proceedings of the 5th International Conference on Genetic Algorithms
Shall We Repair? Genetic AlgorithmsCombinatorial Optimizationand Feasibility Constraints
Proceedings of the 5th International Conference on Genetic Algorithms
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Integrated Facility Design Using an Evolutionary Approach with a Subordinate Network Algorithm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Large-Scale Permutation Optimization with the Ordering Messy Genetic Algorithm
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Convergence Models of Genetic Algorithm Selection Schemes
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Proceedings of the 6th International Conference on Genetic Algorithms
The random keys genetic algorithm for complex scheduling problems
The random keys genetic algorithm for complex scheduling problems
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
Robust and Parallel Solving of a Network Design Problem
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Proceedings of the 2003 ACM symposium on Applied computing
Redundant representations in evolutionary computation
Evolutionary Computation
Orientation matters: how to efficiently solve ocst problems with problem-specific EAs
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
'Adaptive Link Adjustment' Applied to the Fixed Charge Transportation Problem
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
On Optimal Solutions for the Optimal Communication Spanning Tree Problem
Operations Research
New insights into the OCST problem: integrating node degrees and their location in the graph
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
On the bias and performance of the edge-set encoding
IEEE Transactions on Evolutionary Computation
The property analysis of evolutionary algorithms applied to spanning tree problems
Applied Intelligence
Comparing evolutionary computation techniques via their representation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Nonlinear network optimization: an embedding vector space approach
IEEE Transactions on Evolutionary Computation
New hybrid genetic algorithm for solving optimal communication spanning tree problem
Proceedings of the 2011 ACM Symposium on Applied Computing
On a property analysis of representations for spanning tree problems
EA'05 Proceedings of the 7th international conference on Artificial Evolution
A memetic algorithm for the quadratic multiple container packing problem
Applied Intelligence
NP-Completeness of deciding binary genetic encodability
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
EvoGeneSys, a new evolutionary approach to graph generation
Applied Soft Computing
Effect of solution representations on Tabu search in scheduling applications
Computers and Operations Research
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When using genetic and evolutionary algorithms for network design, choosing a good representation scheme for the construction of the genotype is important for algorithm performance. One of the most common representation schemes for networks is the characteristic vector representation. However, with encoding trees, and using crossover and mutation, invalid individuals occur that are either under- or over-specified. When constructing the offspring or repairing the invalid individuals that do not represent a tree, it is impossible to distinguish between the importance of the links that should be used. These problems can be overcome by transferring the concept of random keys from scheduling and ordering problems to the encoding of trees. This paper investigates the performance of a simple genetic algorithm (SGA) using network random keys (NetKeys) for the one-max tree and a real-world problem. The comparison between the network random keys and the characteristic vector encoding shows that despite the effects of stealth mutation, which favors the characteristic vector representation, selectorecombinative SGAs with NetKeys have some advantages for small and easy optimization problems. With more complex problems, SGAs with network random keys significantly outperform SGAs using characteristic vectors.This paper shows that random keys can be used for the encoding of trees, and that genetic algorithms using network random keys are able to solve complex tree problems much faster than when using the characteristic vector. Users should therefore be encouraged to use network random keys for the representation of trees.