Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Explicitly defined introns and destructive crossover in genetic programming
Advances in genetic programming
The evolution of size and shape
Advances in genetic programming
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Analysis of the Causes of Code Growth in Genetic Programming
Genetic Programming and Evolvable Machines
An Overview of Evolutionary Computation
ECML '93 Proceedings of the European Conference on Machine Learning
Accurate Replication in Genetic Programming
Proceedings of the 6th International Conference on Genetic Algorithms
Complexity Compression and Evolution
Proceedings of the 6th International Conference on Genetic Algorithms
Exons and Code Growth in Genetic Programming
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Introns in Nature and in Simulated Structure Evolution
Biocomputing and emergent computation: Proceedings of BCEC97
Fitness Causes Bloat: Mutation
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Evolution of robustness in digital organisms
Artificial Life
Code growth, explicitly defined introns, and alternative selection schemes
Evolutionary Computation
Code growth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
The root causes of code growth in genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Comparing genetic robustness in generational vs. steady state evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Growth of self-canceling code in evolutionary systems
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Dynamics of evolutionary robustness
Proceedings of the 8th annual conference on Genetic and evolutionary computation
DECA: THE DOPING-DRIVEN EVOLUTIONARY CONTROL ALGORITHM
Applied Artificial Intelligence
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Robustness, evolvability, and accessibility in linear genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Genetic Programming and Evolvable Machines
Robustness and evolvability of recombination in linear genetic programming
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
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Results from the artificial life community show that under some conditions evolving populations converge on broader, but less fit peaks in the fitness landscape and avoid more fit, but narrower peaks. Results from the evolutionary computation community show that over time genotypes evolve to become more resilient, where resiliency (or genetic robustness) is defined as the ability of an individual to resist the potentially negative effects of genetic operations. This article demonstrates a previously unobserved evolutionary dynamic: in populations initially favoring a low, broad fitness peak, increases in resiliency result in the population shifting to a higher, narrower fitness peak. In these cases increasing resiliency is a necessary precondition for finding narrower peaks. If increasing resiliency is restricted, for example by restricting growth, populations fail to shift to the narrower peak and remain stuck on the broader, less fit peaks. Thus, restricting growth or other resiliency-enhancing strategies may significantly inhibit evolutionary search by making it impossible for an evolutionary algorithm to find solutions represented by better, but narrower, peaks.