Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Efficiently representing populations in genetic programming
Advances in genetic programming
A Space-Economical Suffix Tree Construction Algorithm
Journal of the ACM (JACM)
Foundations of genetic programming
Foundations of genetic programming
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Measurement of Population Diversity
Selected Papers from the 5th European Conference on Artificial Evolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
The underlying similarity of diversity measures used in evolutionary computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
IEEE Transactions on Neural Networks
Balancing Population- and Individual-Level Adaptation in Changing Environments
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Diversity enhanced particle swarm optimizer for global optimization of multimodal problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Using program data-state scarcity to guide automatic test data generation
Software Quality Control
Ectropy of diversity measures for populations in Euclidean space
Information Sciences: an International Journal
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
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In this paper we address the problem of defining a measure of diversity for a population of individuals whose genome can be subjected to major reorganizations during the evolutionary process. To this end, we introduce a measure of diversity for populations of strings of variable length defined on a finite alphabet, and from this measure we derive a semi-metric distance between pairs of strings. The definitions are based on counting the number of substrings of the strings, considered first separately and then collectively. This approach is related to the concept of linguistic complexity, whose definition we generalize from single strings to populations. Using the substring count approach we also define a new kind of Tanimoto distance between strings. We show how to extend the approach to representations that are not based on strings and, in particular, to the tree-based representations used in the field of genetic programming. We describe how suffix trees can allow these measures and distances to be implemented with a computational cost that is linear in both space and time relative to the length of the strings and the size of the population. The definitions were devised to assess the diversity of populations having genomes of variable length and variable structure during evolutionary computation runs, but applications in quantitative genomics, proteomics, and pattern recognition can be also envisaged.