Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
A linear space algorithm for computing maximal common subsequences
Communications of the ACM
A Survey And Analysis Of Diversity Measures In Genetic Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Population implosion in genetic programming
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Investigation of brood size in GP with brood recombination crossover for object recognition
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Semantically-based crossover in genetic programming: application to real-valued symbolic regression
Genetic Programming and Evolvable Machines
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Many studies emphasize the importance of genetic diversity and the need for an appropriate tuning of selection pressure in genetic programming. Additional important aspects are the performance and effects of the genetic operators (crossover and mutation) on the transfer and stabilization of inherited information blocks during the run of the algorithm. In this context, different ideas about the usage of lineage and genealogical information for improving genetic programming have taken shape in the last decade. Our work builds on those ideas by introducing an evolution tracking framework for assembling genealogical and inheritance graphs of populations. The proposed approach allows detailed investigation of phenomena related to building blocks, size evolution, ancestry and diversity. We introduce the notion of genetic fragments to represent subtrees that are affected by reproductive operators (mutation and crossover) and present a methodology for tracking such fragments using flexible similarity measures. A fragment matching algorithm was designed to work on both structural and semantic levels, allowing us to gain insight into the exploratory and exploitative behavior of the evolutionary process. The visualization part which is the subject of this paper integrates with the framework and provides an easy way of exploring the population history. The paper focuses on a case study in which we investigate the evolution of a solution to a symbolic regression benchmark problem.