Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity on the Gradualist Framework of Evolution (Vienna Series in Theoretical Biology)
Evolvability Suppression to Stabilize Far-Sighted Adaptations
Artificial Life
Embedded cartesian genetic programming and the lawnmower and hierarchical-if-and-only-if problems
Proceedings of the 8th annual conference on Genetic and evolutionary computation
On solving hierarchical problems with top down control
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
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We show how a random mutation hill climber that does multi-level selection utilizes transposition to escape local optima on the discrete Hierarchical-If-And-Only-If (HIFF) problem. Although transposition is often deleterious to an individual, we outline two population models where recently transposed individuals can survive. In these models, transposed individuals survive selection through cooperation with other individuals. In the multi-population model, individuals were allowed a maturation stage to realize their potential fitness. In the genetic algorithm model, transposition helped maintain genetic diversity even within small populations. However, the results for transposition on the discrete Hierarchical-Exclusive-Or (HXOR) problem were less positive. Unlike HIFF, HXOR does not benefit from random drift. This led us to hypothesize that two conditions necessary for transposition to enhance evolvability are (i) the presence of local optima and (ii) susceptibility to random drift. This hypothesis is supported by further experiments. The findings of this paper suggest that epistasis and large mutations can sustain artificial evolution in the long-term by providing a way for individuals and populations to escape evolutionary dead ends. Paradoxically, epistasis creates local optima and holds a key to its resolution, while deleterious mutations such as transposition enhance evolvability. However, not all large mutations are equal.