Network random keys: a tree representation scheme for genetic and evolutionary algorithms
Evolutionary Computation
Artificial Life and Molecular Evolutionary Biology
Proceedings of the Third European Conference on Advances in Artificial Life
The Link and Node Biased Encoding Revisited: Bias and Adjustment of Parameters
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
A Comparison of Two Representations for the Fixed Charge Transportation Problem
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Direct Representation and Variation Operators for the Fixed Charge Transportation Problem
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning
Evolutionary Computation
Genetic algorithms, path relinking, and the flowshop sequencing problem
Evolutionary Computation
Edge sets: an effective evolutionary coding of spanning trees
IEEE Transactions on Evolutionary Computation
The edge-window-decoder representation for tree-based problems
IEEE Transactions on Evolutionary Computation
A new memetic algorithm using particle swarm optimization and genetic algorithm
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Hi-index | 0.00 |
This paper investigates on some properties of encodings of evolutionary algorithms for spanning tree based problems. Although debate continues on how and why evolutionary algorithms work, many researchers have observed that an EA is likely to perform well when its encoding and operators exhibit locality, heritability and diversity. To analyze these properties of various encodings, we use two kinds of analytical methods; static analysis and dynamic analysis and use the Optimum Communication Spanning Tree (OCST) problem as a test problem. We show it through these analysis that the encoding with extremely high locality and heritability may lose the diversity in population. And we show that EA using Edge Window Decoder (EWD) has high locality and high heritability but nevertheless it preserves high diversity for generations.